Deep learning techniques for biomedical and health informatics

Studies in Big Data 68 Sujata Dash · Biswa Ranjan Acharya · Mamta Mittal · Ajith Abraham · Arpad Kelemen   Editors Deep Learning Techniques for Biomedical and Health Informatics Studies in Big Data Volume 68 Series Editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland The series “Studies in Big Data” (SBD) publishes new developments and advances in the various areas of Big Data- quickly and with a high quality The intent is to cover the theory, research, development, and applications of Big Data, as embedded in the fields of engineering, computer science, physics, economics and life sciences The books of the series refer to the analysis and understanding of large, complex, and/or distributed data sets generated from recent digital sources coming from sensors or other physical instruments as well as simulations, crowd sourcing, social networks or other internet transactions, such as emails or video click streams and other The series contains monographs, lecture notes and edited volumes in Big Data spanning the areas of computational intelligence including neural networks, evolutionary computation, soft computing, fuzzy systems, as well as artificial intelligence, data mining, modern statistics and Operations research, as well as self-organizing systems Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output ** Indexing: The books of this series are submitted to ISI Web of Science, DBLP, Ulrichs, MathSciNet, Current Mathematical Publications, Mathematical Reviews, Zentralblatt Math: MetaPress and Springerlink More information about this series at http://www.springer.com/series/11970 Sujata Dash Biswa Ranjan Acharya Mamta Mittal Ajith Abraham Arpad Kelemen • • • • Editors Deep Learning Techniques for Biomedical and Health Informatics 123 Editors Sujata Dash Department of Computer Science North Orissa University Takatpur, Odisha, India Mamta Mittal Computer Science and Engineering Department G B Pant Government Engineering College New Delhi, Delhi, India Biswa Ranjan Acharya School of Computer Science and Engineering KIIT Deemed to University Bhubaneswar, Odisha, India Ajith Abraham Scientific Network for Innovation and Research Excellence Machine Intelligence Research Labs Auburn, AL, USA Arpad Kelemen Department of Organizational Systems and Adult Health University of Maryland Baltimore, MD, USA ISSN 2197-6503 ISSN 2197-6511 (electronic) Studies in Big Data ISBN 978-3-030-33965-4 ISBN 978-3-030-33966-1 (eBook) https://doi.org/10.1007/978-3-030-33966-1 © Springer Nature Switzerland AG 2020 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Preface Overview Biomedical and Health Informatics is an emerging field of research at the intersection of information science, computer science, and health care Health care informatics and analytics is a new era that brings tremendous opportunities and challenges due to easily available plenty of biomedical data for further analysis The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by efficiently analyzing of abundant biomedical, and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle Earlier, it was common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms (deep learning techniques) allow to automatically learning the pattern and representation of the given data for the development of such model Deep learning methods with multiple levels of representation in which at each level the system learn higher abstract level representation Deep learning based algorithms has demonstrated great performance to a variety of areas including computer vision, image processing, natural language processing, speech recognition, video analysis, biomedical and health informatics etc Deep learning approaches such as neural networks such as deep belief network, convolutional neural network, deep auto-encoder, and deep generative networks have emerged as powerful computational models These have shown significant success in dealing with massive data for large number of applications due to their capability to extract complex hidden features and learn efficient representation in unsupervised setting The book will play a vital role in improvising human life to a great extent All the researchers and practitioners those who are working in field of biomedical and health informatics, and deep learning will be highly benefited This book would be a good collection of state-of-the-art approaches for deep learning based biomedical and health related applications It will be very beneficial for the new researchers and practitioners working in the field to quickly know the best performing methods They would be able to compare different approaches and can carry forward their v vi Preface research in the most important area of research which has direct impact on betterment of the human life and health This book would be very useful because there is no book in the market which provides a good collection of the state-of-the-art methods of deep learning based models for biomedical and health informatics as Deep learning is recently emerged and very un-matured field of research in biomedical and healthcare This book, Deep Learning Techniques for Biomedical and Health Informatics, aims to present discussions on various applications of deep learning relating to the Biomedical and Health Informatics problems and suggest latest research methodologies and emerging developments to benefit the researchers and practitioners In this volume, 49 researchers and practitioners of international repute have presented latest research developments, current trends, state of the art reports, case studies and suggestions for further development in the field of biomedical and health informatics, and deep learning Objective The purpose of this book is to report the latest advances and developments in the field of biomedical and health informatics, and deep learning The book comprises the following three parts: • Deep Learning for Biomedical Engineering and Health Informatics • Deep Learning and Electronics Health Records • Deep Learning for Medical Image Processing Organization There are 16 chapters in Deep Learning Techniques for Biomedical and Health Informatics They are organized into three parts, as follows: • Part One: Deep Learning for Biomedical Engineering and Health Informatics This part has a focus on deep learning paradigms and its application in biomedical and health informatics, clinical decision support systems, disease diagnosis and monitoring systems and recommender systems for health informatics There are six chapters in this part The first chapter looks into the application of deep learning to healthcare data in the task like information and relation extraction The second and third contribution focus on discovery of biomedical named entities from many biomedical text mining task applying deep learning techniques The fourth chapter introduces deep learning and developments in neural network and then discusses its applications in healthcare Preface vii and its relevance in biomedical informatics and computational biology research in public health domain The fifth chapter discusses various existing deep learning techniques and their applications for decision support in clinical systems The sixth chapter discusses the challenges and issues of health recommender system • Part Two: Deep Learning and Electronics Health Records The second part comprises seven chapters The first contribution discusses about the design and implementation of explainable deep learning system for healthcare using HER The second chapter audits the deep learning strategies connected with EHR information examination and induction The third chapter contribution focus on the extensive application of deep learning in many domains, including bioinformatics for the analysis and classification of biomedical imaging data, sequence data from omics and biomedical signal processing The fourth chapter discusses advanced distributed security techniques such as blockchain to protect the health data from unauthorized access and the fifth contribution presents CNN based classification for malaria disease to classify the blood films into infected and normal blood films The sixth chapter presents deep reinforcement learning based approach for complete health care recommendations including medicines to take, doctors to consult, nutrition to acquire and activities to perform that consists of exercises and preferable sports The seventh contribution presents the advantages in dealing with text-based extractions and retrievals using deep learning techniques • Part Three: Deep Learning for Medical Image Processing There are three chapters in this part The first chapter discusses several deep learning architectures which can be effectively used for HRV signal analysis for the purpose of detection of diabetes The second chapter discusses the issues and challenges of DL approaches for analysing biomedical images and its application for classification, registration and segmentation The last chapter gives an overview of deep learning-based segmentation algorithms with a special reference to brain tumor classification, various challenges, along with its future scope Target Audiences The current volume is a reference text aimed to support a number of potential audiences, including the following: • Researchers in this field who wish to have the up-to-date knowledge of the current practice, mechanisms, and research developments • Students and academicians of biomedical and informatics field who have an interest in further enhancing the knowledge of the current developments viii Preface • Industry and peoples from Technical Institutes, R&D Organizations, and working in the field of machine learning, deep learning, biomedical engineering, health informatics, and related fields Baripada, Odisha, India Bhubaneswar, Odisha, India New Delhi, India Auburn, AL, USA Baltimore, MD, USA Sujata Dash Biswa Ranjan Acharya Mamta Mittal Ajith Abraham Arpad Kelemen Acknowledgements The editors would like to acknowledge the help of all the people involved in this project and, more specifically, to the reviewers who took part in the review process Without their support, this book would not have become a reality First, the editors would like to thank each one of the authors for their time, contribution, and understanding during the preparation of the book Second, the editors wish to acknowledge the valuable contributions of the reviewers regarding the improvement of quality, coherence, and content presentation of chapters Last but not least, the editors wish to acknowledge the love, understanding, and support of their family members during the preparation of the book Baripada, Odisha, India Bhubaneswar, Odisha, India New Delhi, India Auburn, AL, USA Baltimore, MD, USA Sujata Dash Biswa Ranjan Acharya Mamta Mittal Ajith Abraham Arpad Kelemen ix Automated Brain Tumor Segmentation in MRI Images Using … Table Best chromosome selected by Genetic Algorithm with their classification accuracy 369 Sr no Feature number selected Classification accuracy 14,16,6,12,9,7,20 74.0331 13,19,20,4,6,20,7 74.0222 15,19,17,12,14,7,20 73.3425 20,17,9,18,3,15,19 73.2432 9,20,15,5,19,18,3 73.2044 1,2,1617,16,1,11 73.0 3,7,16,20,19,6,15 75 9,16,3,13,14,6,3 72.0994 9,20,15,5,19,18,3 74.7238 10 18,20,19,6,10,5,13 71.2707 5.5 Neuro Fuzzy Modeling Neuro Fuzzy concept was developed in 1995 by J.S.R Jang The hybridization of neuro-fuzzy is the most fruitful integration of the Soft Computing techniques Neuro Fuzzy system combines benefits of both fuzzy system and neural network Fuzzy logic is capable of modeling vagueness, handling uncertainty and supporting humantype reasoning The Adaptive Network based Fuzzy Inference System (ANFIS) uses a Takagi Sugeno Fuzzy Inference System and it has five layers as shown in Fig 17 The first hidden layer is used for mapping of the input variable to their corresponding membership functions To calculate antecedent of rule, T-norm is applied in the second hidden layer Final shape of membership function is also tuned in the second layer The third hidden layer is concerned about normalization of rule strength Fig 17 Layered architecture of anfis 370 M Sharma and N Miglani Fig 18 GUI of ANFIS editor 5.6 ANFIS Editor ANFIS Editor GUI can be used for initialization of FIS properties To start ANFIS editor in MATLAB type: anfisedit Figure 18 shows GUI of ANFIS editor in which no of inputs are and corresponding to input there is output Each input has two membership functions of custom type There are different panel in GUI such as loading the data, generating FIS, training FIS, and testing FIS where loading of data is the first step Data should be in matrix form and can be either taken from file or workspace 5.7 Training and Testing Phase Proposed system comprises two steps: In first step, training is done and in the second step, testing is done as shown in Fig 19 Automated Brain Tumor Segmentation in MRI Images Using … Training Image Data Set Feature Extractor Feature Stored in Database 371 Test Image [X1 X2……X7] Feature Extractor Feature Extractor [X1 X2……X7] Feature of test image [Y1, Y2……Y7] Feature Extractor Feature Extractor [X1 X2……X7] Best Match searches in database [X1 X2……X7] Fig 19 Schematic diagram for MRI training and testing In training phase, features from different images are extracted using GLCM and are reduced to feature subset using Genetic Algorithm and then store them in the database along with the corresponding output Total 57 images are used to train proposed system When a query image comes for tumor identification, firstly its GLCM image features are extracted and are finally send to recognizer of proposed work for finding the best suitable match After, finding suitable match, corresponding output will be generated Output means which type of tumor is there and grade of tumor as well [58] 372 M Sharma and N Miglani Image Segmentation and Classification Methods Region Based Region growing and splitting Region merging method Watershed segmentation Level set method Active Contour Edge Based Methods Gradient based methods Gray Histogram Unsupervised Methods K means FCM ANT Tree Algorithm Supervised Methods KNN SVM PCA Neural network Based Feed Forward learning Single layer Multi-Layer Feed Back Learning ART models Fig 20 Classification of MRI brain image segmentation methods 5.8 Image Segmentation Methods [59–61] There is various image segmentation methods as shown in Fig 20 5.9 Fuzzy C-Means Segmentation Fuzzy C-Means Segmentation (FCM) is a well-known clustering algorithm, used in pattern recognition [62–68] FCM has an advantage that it is not necessary that one data belongs to only one cluster instead one data can share more than one cluster Basic FCM features are shown in Fig 21 The FCM algorithm partitions finite collection of n elements X = {x1 , …, xn } into a collection of c fuzzy clusters with respect to some given criterion Step 1: Initialization Initialize membership function means assign cluster to each one of them For example-Four clusters (C1, C2, C3, C4) have been used for detecting four type of brain tumor C µ j (xi ) = j=1 (10) Automated Brain Tumor Segmentation in MRI Images Using … 373 Start Initialize membership matrix Calculate centroids Calculate dissimilarity between the data points and centroid using Euclidean distance Update new membership matrix No Is previous cluster center same as new cluster center? yes Stop Fig 21 Flow chart of FCM algorithm where i n J C µ j (xi ) = 1, 2, 3, … n represent no of elements to be partition into clusters = 1, 2, … C represents no of clusters in which elements are to be partitioned represents degree to which element xi belongs to cluster Cj Step 2: Calculate centroids cj = µ j (xi) i i µ j (xi) m xi m (11) where, m is fuzzification parameter and its value lies between 1.25 and (generally) Step 3: Calculate dissimilarity between the data points and centroid using Euclidean distance 374 M Sharma and N Miglani Di = (x2 − x1 )2 + (y2 − y1 )2 (12) Step 4: Update new membership matrix using the eq µ j (xi ) = d ji c j=1 m−1 d ji m−1 (13) Step 5: Go back to step 2, unless centroids are not changing In Fig 22, four clusters are represented by four colors-red, blue, purple and green and cluster center is represented by “X” • Shape feature can also be used to increase classification accuracy Get extra information from patient like history, age to increase classification accuracy • Modified Sugeno type ANFIS can be used Fig 22 Output after FCM segmentation Automated Brain Tumor Segmentation in MRI Images Using … 375 Various Challenges Faced by Deep Learning Though deep learning in itself is a domain with numerous benefits and has large number of practical applications yet to attain those benefits, one might encounter some challenges as discussed below: 6.1 Huge Amount of Data The human brain requires lots of information and experiences to reach to any outcome On similar pattern, artificial neural networks demands huge amount of data for training and learning Huge dataset is beneficial to obtain accurate and precise results Deep learning classifier relies heavily on the magnitude and quality of dataset available If limited data or information is available, it could directly hamper the success ratio of deep learning, specifically in medical domains [69] Although, huge dataset is a crucial concern, yet another challenge lies in generating such data for medical imaging as it depends on the observations and interpretations provided by experts of that field In order to minimize inaccuracies and human errors, it is important to consider multiple experts opinions This would become difficult if field experts are not available Moreover, in extreme cases of rare diseases, sufficient cases might not be available One more issue could be unbalancing of data as if it is the case of rare disease, data set could be unprecedented, and in which case an imbalance may supervene 6.2 Domain Specific and Multi-tasking In deep learning, training the data can yield productive and precise results, but only for a specific problem In the current scenario, deep learning approach is highly domain-specific in such a way that if one requires solution for similar kind of problems or patterns, one has to re-assess and re-train the data all over Although, the approach is efficient enough for solving some specific problem, yet it is inflexible to accommodate multi-tasking Research is going on to focus multi-tasking without the need of revising complete architecture Multi-Task Learning (MTL) and Progressive Neural Networks are being explored to bring some amelioration in this aspect 6.3 Deep Learning Is Intrinsically a Black Box Deep learning algorithms bought new hopes in the field of medical imaging and triggered new opportunities It provided the solution for the problems which were 376 M Sharma and N Miglani previously considered to be unsolvable by conventional approaches Still, it has its own shortcomings One of them is Black-Box problem Although a clear vision is there about what input has been fed to the network, and how they would be combined together yet an output generation is quite complex and there is no clear understanding about how output has been generated Identifying inputs, applying model parameters, and building the model is available but how the model is actually working is quite an issue to understand For such reasons, the domain becomes weak in the situations where verification is the foremost requirement as internal manipulations are hidden from user 6.4 Optimizing Hyper-parameters When the values of parameters are set before the learning process begins, these are called hyper-parameters If a small change is done in these values, it could largely affect the model performance When real life problems are considered, default value of parameters cannot help building accurate results It can hamper the system performance significantly If small number of hyper-parameters are considered and are tuned manually instead of optimizing them with standard methods, could also raise a performance issue 6.5 Requires High Performance Hardware Deep learning requires high capacity hardware which is costly and demands huge power consumption as well 6.6 Less Flexibility Deep neural network can be trained to one domain only It cannot adapt to another domain For different problem, it again requires training of neurons Research Issues and Future Perspectives Processing Power, Big Data and Deep Learning Algorithms based on human brain are three key features that are stimulating the revolution of deep learning Undoubtedly, the benefits achieved by deep learning are remarkable and for attaining those benefits, human efforts and cost incur is also high Large scale companies and different research laboratories with prominent hospitals are also engaging and functioning Automated Brain Tumor Segmentation in MRI Images Using … 377 together towards reaching the most favorable unravelments in medical fields Numerous companies namely, Hitachi, Siemen etc have already step forward for putting high expenses in the domain For detection of pediatric brain disorders, GE Healthcare with Bostons Children Hospital is developing smart imaging technology Even research labs are expending money for delivering potent image-based applications 7.1 Enhancements in Deep Learning Approach Deep learning technology relies on supervised learning approach Nonetheless, illustrations of medical data, precisely, medical images are not available often These are the cases when either disease occurrence is rare or field expert is not available To overpower as issue of data unavailability, it is crucial to switch from supervised to either unsupervised or semi-supervised learning method If training approach is shifted to unsupervised or semi-supervised approach, specifically in medical fields, an accuracy and precision of final results might come on stake Though efforts are being put in this aspect, yet some rigid solution has not been attained to tackle with an issue of inaccuracies There are infinite opportunities lying for the scope of improvements and modifications 7.2 Big Image Data Exploitation There is a requirement of huge dataset for applying deep learning methods, and availability of such huge data in itself is a crucial and difficult task Illustration of real world data is easy in comparison to medical image data For instance, illustration of objects, distinction of men or women in real world is a negligible task to whereas interpretation of medical images requires field expertise as well as it is costly affair which demands lot of time for processing In fact, not only an opinion of single expert but a multiple experts for same data are required for gaining accuracies and peculiarities in manipulating image data One more issue could lie in whether data is available or not in case where diseases are rare In such cases, it becomes more difficult to get large amount of dataset The solution for above- suggested problem could be the sharing of data by different healthcare service providers as far as possible In this way problem of data access could be minimized 7.3 Pervasive Inter-organization Collusion Even though numerous predictions about benefits and growth of deep learning in medical image field are being made by stakeholders, yet replacement of human with machines or tools will always remain a debatable issue Significant improvements in 378 M Sharma and N Miglani accuracies of analysis and prediction in disease diagnosis by deep learning approach cannot be ignored However, some issues persists which needs immediate attention of researchers Collusion between vendors, field experts and hospitals is unavoidable in order to meet exceptional benefits for enhancing the health quality This would resolve the problem of data availability to the field experts and researchers Another issue contracts an advanced tools and equipments to tackle exhaustive and unlimited healthcare data This would be more helpful in the cases where sensor networks are increasing volume of data in an exponential way 7.4 Privacy and Judicial Concerns Either technical or sociological issues can affect data confidentiality, thus there is an urge of dealing it with both perspectives technical as well as sociological To deal with privacy concerns, HIPAA comes to the mind as far as medical field is concerned HIPAA stands for Health Insurance Portability and Accountability Act of 1996, is an US Legislation It renders patients with the legal rights concerning his/her individual accountable information and providing some standards and protocols to secure their personal details and their use in any form This privacy concern is an absolute need of the current scenario yet it is challenging in terms of how to secure and hide the patient personal information in order to forbid its misuse If some kind of restrictions would prevail on data, then it would limit the content availability, which would further raise an issue of limited dataset and henceforth, would lead to inaccurate results Although it is not mandatory to comply with HIPAA yet secure health information can be stored and maintained as HIPAA covered entity Applicability of HIPAA exists only if Protected Health Information for transactions is transmitted electronically Indian organizations and companies are also being assisted for HIPAA compliance in order to stay ahead in the world of data protection Moreover, health care data is dynamic in nature, thus existing methodologies are insufficient to tackle the problem Performance Comparison Diagnostic accuracy of different image segmentation algorithm can be analyzed (as shown in Fig 23 and Table 5) in terms of following parameters: Sensitivity = True Positive/(True Positive + False Negative) ∗ 100% Specificity = True Negative/(True Negative + False Positive) ∗ 100% Accuracy = (True Positive + True Negative)/(True Positive + True Negative + False Positive + False Negative) ∗ 100 Automated Brain Tumor Segmentation in MRI Images Using … 379 Fig 23 Comparative analysis between deep learning and other segmentation methods Table Comparative analysis between deep learning and other segmentation methods (also, refer Fig 23) Algorithms Sensitivity (%) Specificity (%) Accuracy (%) Fuzzy C means segmentation 96.1 93.4 86.16 ANFIS + Genetic 95.1 93.1 90.1 K-Mean + FCM 80.1 93.32 83.4 Deep learning (CNN) 97.01 96.1 97.17 Conclusion For the automation of daily life tasks, deep learning has gained much popularity in recent years In the upcoming years most of the routine jobs would be performed using automatic devices rather than manual work This chapter yields an overview of different segmentation methods for images Deep learning methods are more efficient and can address problem in better way than other algorithms Deep learning provides 380 M Sharma and N Miglani improvised results in comparison to conventional approaches of machine learning In this chapter we discuss various phases in brain tumor segmentation Each phase has been discussed in brief Various deep learning algorithms has been compared with their relevant advantages and disadvantages This chapter also discusses the reasons behind slow growth of deep learning in medical field Various solutions have been proposed by different researchers In the last section various research open issues and future directions have been addressed 10 Future Scope (1) More features can be embedded to enhance classification precision Shape feature is one of those features which can help raise an accuracy level of classification being done Get extra information from patient like history, age to increase classification accuracy (2) More efficient deep learning Model Major problem in automatic brain tumor segmentations the similarity between background and tumor pixels Some background pixels are misclassified as brain tumor pixels So, in future a more efficient deep learning model can be developed that can differentiate between tumor and background pixels with more accuracy (3) To train Deep CNN a more efficient loss function can be chosen A more effective loss function helps in differentiating between background and tumor pixels with improved accuracy (4) Colored images may also be considered This study targets only grey-scale images Besides, it could be intensified to augment colored images References Zikic, D., Ioannou, Y., Brown, M., Criminisi, A.: Segmentation of brain tumor tissues with convolutional neural networks In: Proceedings of MICCAI workshop on Multimodal Brain Tumor Segmentation Challenge (BRATS), pp 36–39 (2014) Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MRI images IEEE Trans Med Imaging 35(5), 1240–1251 (2016) Central Brain Tumor Registry of the United States (CBTRUS), Fact Sheet available at (2011) http://www.cbtrus.org/factsheet.html Christ, J.M., Parvathi, R.M.S.: Brain tumors: an engineering perspective IJCSI 9(4), 392–396 (2012) Schmidt, F.E.W.: Development of a time-resolved optical tomography system for neonatal brain imaging Ph.D thesis, Chapter-2, pp 25–34 (1999) Thurnher, M.M., Thurnher, S.A., Fleischmann, D., Steuer, A., Rieger, A., Helbich, T., Trattnig, S., Schindler, E., Hittmair, K.: Comparison of T2-weighted and fluid-attenuated inversionrecovery Am Soc Neuroradiol 1601–1609 (1997) Doolittle, N.D.: State of the science in brain tumor classification Semin Oncol Nurs 20, 224–230 (2004) Automated Brain Tumor Segmentation in MRI Images Using … 381 Wen, P.Y., Teoh, S.K., Black, P.M.: Brain tumors: an encyclopedic approach Cancer Neurol Clin Pract 217–248 (2001) Chandrasoma, P.C.P.: Stereotactic brain biopsy W J Med 1–5 (1991) 10 Kong, N.S.P., Ibrahim, H., Hoo, S.C.: A literature review on histogram equalization and its variations for digital image enhancement Int J Softw Eng Res Pract 1(2), 386–389 (2013) 11 Singaravel, S., Suykens, J., Geyer, P.: Deep-learning neural-network architectures and methods: Using component-based models in building-design energy prediction Adv Eng Inform 38, 81–90 (2018) 12 Du, X., Cai, Y., Wang, S., Zhang, L.: Overview of deep learning In: 31st Youth Academic Annual Conference of Chinese Association of Automation Wuham, China, 11–13 Nov 2016, pp 159–164 13 Ishak, N.F., Logeswaran, R., Tan, W.H.: Artifact and noise stripping on low-field brain mri Int J Biol Biomed Eng 2(2), 59–68 14 Nobi, M.N., Yousuf, M.A.: A new method to remove noise in magnetic resonance and ultrasound images J Sci Res 3(1), 81–89 (2011) 15 Devasena, C.L., Hemalatha, M.: Noise removal in magnetic resonance images using hybrid KSL filtering technique Int J Comput Appl 27(8), 1–4 (2011) 16 Kumar, S., Kumar, P., Gupta, M., Nagawat, A.K.: Performance comparison of median and wiener filter in image de-noising Int J Comput Appl 12(4), 27–31 (2010) 17 Bhatia, A., Kulkarni, R.K.: High density salt and pepper noise removal through improved adaptive median filter Int Conf Comput Sci Inform Technol (CSIT-2012) 197–200 (2012) 18 Bagade, S.S., Shandilya, V.K.: Use of histogram equalization in image processing for image enhancement Int J Softw Eng Res Pract 6–10 (2011) 19 Chen, S.D.: Contrast enhancement using brightness preserving bi-histogram equalization IEEE Trans Consum Electron 1, 1–8 (1997) 20 Wang, C., Zhongfu, Y.: Brightness preserving histogram equalization with maximum entropy: a variational perspective IEEE Trans Consum Electron 51(4), 1326–1334 (2005) 21 Ning, C.Y., Liu S.F., Qu, M.: Research on removing noise in medical image based on median filter method IEEE Explore 384–388 (2009) 22 Sawant, H.K., Deore, M.: A comprehensive review of image enhancement techniques Int J Comput Technol Electron Eng 1(2), 34–38 (2012) 23 Gonzalez, R.C., Woods, R.E.: Digital image processing, 2nd edn Prentice Hall (2002) 24 Chen, S.D., Ramli, R.: Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation IEEE Xplore, 1301–1309 (2001) 25 Dykstra, C., Das, M.: The use of image morphing to improve the detection of tumors in emission imaging Nucl Sci Symp 3, 1781–1785 (1998) 26 Marr, D., Hildreth, E.: Theory of edge detection Proc Roy Soc Lond B 187–217 (1980) 27 Canny, J.: A computational approach to edge detection IEEE Trans Pattern Anal Mach Intell 6, 679–698 (1986) 28 Schunck, B.G.: Edge detection with Gaussian filters at multiple scales IEEE Comput Soc Work Comp Vis.208–210 (1987) 29 Bergholm, F.: Edge focusing IEEE Trans Pattern Anal Mach Intell PAMI-09, 726–741 (1987) 30 Lacroix, V.: The primary raster: A Multiresolution Image Description In: 10th International Conference on Pattern Recognition, pp 903–907 (1990) 31 Williams, D.J., Shah, M.: Edge contours using multiple scales Comput Vis Graph Image Process 51, 256–274 (1990) 32 Goshtasby, A., Marr, D.: On edge focusing Image visualization Computer 12, 247–256 33 Deng, G., Cahill, L.W.: An adaptive Gaussian filter for noise reduction and edge detection In: Proceedings IEEE Nuclear Science Symposium, pp 1615–1619 (1994) 34 Bennamoun, M., Boashash, B., Koo, J.: Optimal parameters for edge detection Proc IEEE Int Conf SMC 2, 1482–1488 (1995) 35 Heric, D., Zazula, D.: Combined edge detection using wavelet transform and signal registration Elsevier J Image Vis Comput 25, 652–662 (2007) 382 M Sharma and N Miglani 36 Shih, M.Y., Tseng, D.C.: A wavelet based multi resolution edge detection and tracking Elsevier J Image Vis Comput 23, 441–451 (2005) 37 Bezdek, J.C., Chandrasekhar, R., Attikiouzel, Y.: A geometric approach to edge detection IEEE Trans Fuzzy Syst 6(1), 52–75 (1998) 38 Wu, J., Yin, Z., Xiong, Y.: The fast multilevel fuzzy edge detection of blurry images IEEE Signal Process Lett 14(5), 344–347 (2007) 39 Lu, S., Wang, Z., Shen, J.: Neuro-fuzzy synergism to the intelligent system for edge detection and enhancement Elsevier J Pattern Recogn 36, 2395–2409 (2003) 40 Shrivakshan, G.T., Chandrasekar, C., Bhandarkar, S.M.: An edge detection technique using genetic algorithm-based optimization Pattern Recogn 27(9), 1159–1180 (1994) 41 Zhang, Y., Potter, W.D.: Comparison of various edge detection techniques used in image processing IJCSI Int J Comput Sci Issues 9(5), 269–276 (2012) 42 Becerikli, Y., Karan, T.M., Cabestany, J., Prieto, A., Sandoval, D.F.: A new fuzzy approach for edge detection IWANN 2005, 943–951 (2005) 43 Anver, M.M., Stonie, R.J.: Evolutionary learning of a fuzzy edge detection algorithm based on multiple masks Springer, vol 12, pp 1–13 (2005) 44 Suliman, C., Boldi¸sor, C., B˘az˘avan, R., Moldoveanu, F.: A fuzzy logic based method for edge detection Eng Sci 4, 159–164 (2011) 45 Sharifi, M., Fathy, M., Mahmoudi, M.T.: A classified and comparative study of edge detection algorithms In: Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC.02) IEEE, pp 1–4 (2002) 46 Yu-Qian, Z., Wei-Hua, G., Zhen-Cheng, C., Jing-Tian, T., Ling-Yun, L.: Medical images edge detection based on mathematical morphology In: Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, pp 6492–6495 (2005) 47 Saxena, S., Kumar, S., Sharma, V.K.: Comparative analysis of various edge detection techniques Int J Adv Res Comput Sci Softw Eng 3(6), 758–761 (2013) 48 Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural Features for Image Classification IEEE Trans Syst Man Cybern 610–621 (1973) 49 Prasetiyo, Khalid, M., Yusof, R., Meriaudean, F.: A comparative study of feature extraction methods for wood texture classification SITIS, IEEE Conf 23–29 (2010) 50 Nithya, R., Santhi, B.: Comparative study on feature extraction method for breast cancer classification J Theor Appl Inf Technol 33(2), 220–226 (2011) 51 Chadha, A., Mallik, S., Johar, R.: Comparative study and optimization of feature-extraction techniques for content based image retrieval Int J Comput Appl 52(20), 35–42 (2012) 52 Ramamurthy, B., Chandran, K.R., Aishwarya, S., Janaranjani, P.: CBMIR: content based image retrieval using invariant moments, GLCM and grayscale resolution for medical images Eur J Sci Res 460–471 (2010) 53 Hamza, R.M., Al-Assadi, T.A.: Genetic algorithm to find optimal GLCM features Inf Technol Univ Babylon Iraq pp 1–16 (2012) 54 Jolliffe, I.T., Potter, W.D.: Principal Component Analysis, 2nd edn, pp 1–5 Springer, New York (2002) 55 Scholkopf, B., Smola, A., Muller, K.R.: Kernel Principal Component Analysis, pp 327–352 IT Press, Cambridge, MA (1999) 56 Shapiro, V.A., Veleva, P.K., Sgurev, V.S.: An adaptive method for image thresholding In: 11th IAPR International Conference on Image, Speech and Signal Analysis, pp 696–699 (1992) 57 Sezgin, Mehmet, Sankur, Bulent: Survey over image thresholding techniques and quantitative performance evaluation J Electron Imaging 13, 146–165 (2004) 58 Elaiza, N., Khalid, A., Ibrahim, S., Manaf, M.: Comparative study of adaptive network-based fuzzy inference system (ANFIS), k-nearest neighbors (k-NN) and fuzzy c-means (FCM) for brain abnormalities segmentation Int J Comput 5(4), 513–524 (2011) 59 Zhang, J., Morgan, N.: Stochastic model based image segmentation using Markov random fields and multi-layerperceptrons IEEE Signal Process 1–8 (1990) 60 Azmi, R., Norozi, N.: A new markov random field segmentation method for breast lesion segmentation in MR images J Med Signals Sens 1(3), 156–164 (2011) Automated Brain Tumor Segmentation in MRI Images Using … 383 61 Prastawa, M., Bullitt, E., Gerig, G.: A brain tumor segmentation framework based on outlier detection Med Image Anal 18, 217–231 (2004) 62 Dipali, B.B., Patil, S.N.: Brain tumor mri image segmentation using FCM and SVM techniques Int J Eng Sci Comput 3939–3942 (2016) 63 Kannan, S.R., Ramathilagam, S., Devia, R., Hines, E.: Strong fuzzy C-means in medical image data analysis J Syst Softw 2425–2438 (2012) 64 Zhang, J.G., Ma, K.K., Chong, V.: Tumor segmentation from magnetic resonance imaging by learning via one-class support vector machine IWAIT 207–21 (2004) 65 Garcia, C., Moreno, J.: Kernel based method for segmentation and modeling of magnetic resonance images LNCS 636–645 (2004) 66 Lee, C.H., Schmidt, M., Murtha, A., Bistritz, A., Sander, J., Greiner, R.: Segmenting brain tumors with conditional random fields and support vector machines LNCS 3765, 469–478 (2005) 67 Gibbs, P., Buckley, D.L., Blackband, S.J., Horsman, A.: Tumor volume determination from MR images by morphological segmentation Phys Med Biol 2437–2446 (1996) 68 Letteboer, M., Olsen, O., Dam, E., Willems, P., Viergever, M., Niessen, W.: Segmentation of tumors in magnetic resonance brain images using an interactive multiscale watershed algorithm Acad Radiol 11, 1125–1138 (2011) 69 Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., Larochelle, H.: Brain tumor segmentation with deep neural networks Med Image Anal 35(2017), 18–31 (2017) 70 Web Source: https://www.oreilly.com/library/view/deep-learning/9781491924570/ch04.html 71 Magudeeswaran, V., Ravichandran, C.G.: Fuzzy logic-based histogram equalization for image contrast enhancement Math Eng 1–10 (2013) 72 Vorontsov, A.O., Averkin, A.N.: Comparison of different convolution neural network architectures for the solution of the problem of emotion recognition by facial expression In: Proceedings of the VIII International Conference “Distributed Computing and Grid-technologies in Science and Education” (GRID 2018), Dubna, Moscow region, Russia, Sep 10–14 2018, pp 35–40 73 Agarwal, V.: Analysis of histogram equalization in image preprocessing BIOINFO Hum Comput Interact 1(1), 04–07 74 Yang, Y., Huang, S.: Novel statistical approach for segmentation of brain magnetic resonance imaging using an improved expectation maximization algorithm Optica Appl 125–36 (2006) 75 Vinitski, S., Iwanaga, T., Gonzalez, C.F., Andrews, D., Knobler, R., Curtis, M.: Fast tissue segmentation based on a 4D feature map In: 9th International Conference (ICIAP 97), vol 2, pp 445–452 (1997) 76 Revathy, M., Hemalataha, M.: Efficient method for feature extraction on video processing In: CCSEIT 2012 ACM International Conference, pp 539–543 (2012) Minakshi Sharma received the Ph.D degree in Computer Science from Banasthali University Rajasthan India, in 2015 In 2017, she joined as an Assistant Professor in NIT Kurukshetra in the Department of Computer Engineering She has more than 10 papers to his credit in national and international conferences and journals Her research interests include Deep Learning, Artificial Intelligence, Neural Network, Fuzzy logic Based systems Neha Miglani she has received her Master Degree in Computer Science from Kurukshetra University, India in 2012 Currently, she is working as an Assistant Professor in National Institute of Technology, Kurukshetra, India Her research interest includes Cloud Computing, Neural Networks, Software Reliability ranging from Cost Models, Software Reliability Growth Models, and Reliability metrics, etc ... field of biomedical and health informatics, and deep learning The book comprises the following three parts: • Deep Learning for Biomedical Engineering and Health Informatics • Deep Learning and Electronics... of deep learning based models for biomedical and health informatics as Deep learning is recently emerged and very un-matured field of research in biomedical and healthcare This book, Deep Learning. .. follows: • Part One: Deep Learning for Biomedical Engineering and Health Informatics This part has a focus on deep learning paradigms and its application in biomedical and health informatics, clinical