Enhancing COVID 19 Prediction Using Transfer Learning from Chest X Ray Images Enhancing COVID 19 prediction using transfer learning from Chest X ray images 1st Phuoc Hai Huynh Faculty of Information T[.]
2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Enhancing COVID-19 prediction using transfer learning from Chest X-ray images 2nd Trung-Nguyen Tran 1st Phuoc-Hai Huynh Faculty of Information Technology An Giang University, Vietnam National University Ho Chi Minh City hphai@agu.edu.vn 3rd Van Hoa Nguyen Faculty of Information Technology Deparment of General Planing An Giang Regional General Hospital An Giang University, Vietnam National University Ho Chi Minh City it.bvcd@gmail.com nvhoa@agu.edu.vn Abstract—The pandemic of COVID-19 is expansion and effect for human lives all over the world Although many countries have been vaccinated, the number of new COVID-19 patients infected is still increasing Recently, the detection of COVID-19 early can help find effective treatment plans using machine learning technologies algorithms We propose the transfer learning models to detect pneumonia disease by this virus from chest X-Ray images The public dataset is used in this work, and the new chest X-Ray images of COVID-19 patients are collected by An Giang Regional General Hospital These images enrich the current public dataset and improve the performance prediction Six transfer learning architectures are investigated using locally collected and public dataset The experiment results show that the DenseNet121 transfer learning model outperforms others with the accuracy, precision, recall, F1-scores, and AUC of 98.51%, 98.54%, 98.51%, 98.05% and 99.15%, respectively on the augmented dataset and most algorithms process new data are improved performance Index Terms—COVID-19, transfer learning, imbalanced dataset, X-Ray images I I NTRODUCTION From January 2020 to the present, the COVID-19 pandemic has caused the most high-priority health crisis in human history The disease has effect the world, with over 251 million infections have been and killed more than million worldwide (as of November 2021) [1] The coronavirus causes various symptoms, such as cough, fever, and cause difficulty while breathing in more severe patients These symptoms are very much approximate to the other usual pneumonia [2] Thus, it is sometimes hard to find the difference between common pneumonia and COVID-19 The COVID-19 patients have lung injury and respiratory failure [3] Recognizing COVID-19 patients, isolating and caring for them is a critical technique for better pandemic management In order to detect COVID-19, the RT-PCR test [4] is an accepted standard for coronavirus disease 2019 prediction [5] Nevertheless, RT-PCR is costly as well as time-consuming [6] Therefore, many studies are applied using medical image classification as an effective method for detecting COVID-19 using digital X-Ray images [7]–[9] According to the World Health Organization, X-Ray is currently one of the best available techniques for clinical diagnosis [10] This method is widely used in the COVID19 prediction because it is not only quick but also cheap 978-1-6654-1001-4/21/$31.00 ©2021 IEEE [11] With increasing digital X-Ray images in hospitals, these images are processed by machine learning technologies to support treatment [12], [13] In order to diagnose for COVID-19 patients, the medical image collections are increasingly being used in studies to construct artificial intelligence models to assist clinicians In recent years, the deep learning algorithms analysis of chest X-Ray images enable a promising method for COVID-19 prediction [7], [14] They could identify early COVID-19 disease at risk of severe progression which may facilitate personalized treatment plans However, collecting and publishing the large medical image datasets are challenging tasks for contributing to the development of computeraided design systems in COVID-19 prediction There are some issues in this work, including patient information security, misdiagnosis and the cost of collecting data annotations An alternative method of training deep learning models is transfer learning, which is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task The benefits of this approach are used to initiate deep learning models, next it is tuned using the limited sample dataset to outperform fully trained networks under certain circumstances [15] The purpose of this work is to investigate the usefulness of transfer learning for COVID-19 prediction in An Giang Regional General Hospital through two contributions Firstly, we collected 750 Chest X-Ray images confirmed COVID-19 cases at An Giang Regional General Hospital (from September to 11 November, 2021) This dataset is very helpful to enrich the public X-Ray images dataset for COVID-19 prediction Secondly, six transfer learning models are implemented to detect COVID-19 including VGG16 [16], VGG19 [17], DenseNet121 [18], Xception [19], InceptionV2 [20], and Resnet50 [21] The main idea is to improve the strength of predict models using the representations learned by a previous network to extract meaningful features from new images Secondly, we have conducted a thorough evaluation of our approach in four experiments: (1) the public dataset is used for training and testing; (2) the augmented dataset is created by combining the new X-Ray COVID-19 images and the public dataset It is used to train and test the models; (3) the public dataset is used to train the model, and the local data is used for 398 2021 8th NAFOSTED Conference on Information and Computer Science (NICS) testing only; (4) the augmented dataset is used for training, and the local dataset is used for testing The results have shown that the DensenNet121 model achieves efficient results compared to the other models for predicting COVID-19 Most algorithms process our dataset with improved performance The remainder of this paper is organized in the following manner Proposed model is presented in Section We analyze the experiment and numerical test results are shown in Section 3, and end in Section II PROPOSED MODEL The proposed model consists of four steps: collecting data; data preprocessing; transfer-learning using pretrained deeplearning models: Xception [19], InceptionV2 [20], ResNet50 [21], VGG16 [16], VGG19 [17], and DenseNet121 [18]; and evaluating models This study classified the chest X-Ray images into tree classes as COVID-19, normal and pneumonia The description of the stages is discussed below Fig shows the diagrammatic flow of the proposed model Collect Chest X-ray images (a) NORMAL Data preprocessing (b) PNEUMONIA Transfer Learning Using models Evaluation Predict Covid-19 Fig 1: Experiment pipeline for preprocessing and classification First of all, the public dataset is collected by 5144 samples published by Cohen [22] This dataset currently contains hundreds of frontal view X-Ray images and is the largest public resource for COVID-19 images and prognostic data Therefore, it is a necessary resource to develop and evaluate tools to aid in the treatment of COVID-19 For this dataset, the samples are divided into three classes as follows: 2121 normal, 3735 normal pneumonia, and 576 COVID-19 Second dataset is collected at An Giang Regional General Hospital which treats Covid-19 patients in severe form in An Giang province This dataset contains 850 DICOM (Digital Imaging and Communications in Medicine) X-Ray images COVID-19 The process of labeling the images based on the results of RTPCR testing The patient information are hidden and encoded to security The DICOM images are converted to JPEG format type to reduce size and suitable with transfer learning models In order to convert DICOM images, we used PyDicom library in Python [23] In order to resize the images, a dimension of 224 × 224 × was used Fig shows an example of a COVID-19, pneumonia and normal (c) COVID-19 Fig 2: Samples of the chest X-Ray images Secondly, we implement six transfer learning models including: Xception [19], InceptionV2 [20], ResNet50 [21], VGG16 [16], VGG19 [17], and DenseNet121 [18] The VGG models are proposed by Oxford university’s visual geometry team The VGG architectures are widely used in image classification In this work, VGG16 [16] and VGG19 [17] are used with 16 and 19 convolution layers The DenseNet (Dense Convolutional Network) [18] is an architecture that uses shorter connections between layers on making the networks go even deeper Inside this network, each layer is connected to all other layers that are deeper in the network to enable maximum information flow between the layers of the network The architecture of the network consists of dense blocks and the transition layers stacking Especially, this model usually applies for medical images fields [23]–[25] In this work, we use DenseNet121 architecture that has 121 layers 399 2021 8th NAFOSTED Conference on Information and Computer Science (NICS) The ResNet [21] is designed to handle the vanishing gradient problem It implements skip connections between layers to to train model more efficient In our experiments, we use ResNet50 model with 50 layers The Inception V3 [20] model is a deep architecture learning, which has the depth and width of the network larger to improve using computing resources These inception modules are looped by stacking with layers to reduce dimension Another model is Xception [19] is developed by Google Inc It has been shown to outperform Inception on a large-scale image classification dataset Six models are used to transfer weights learned on ImageNet [26] for COVID-19 prediction model in this work The main idea of the method is to improve classification results using public data and to use the benefits of transfer learning technology It helps to address the limited size of the local dataset In addition, it is also enhancing the training process on modest devices In order to handle imbalanced classes, we calculate and set class weights for models It is a simple and effective method to address this problem For binary classification, class weight is computed by the frequency of the positive and negative classes and inverting them In this work, we use multi-label binarization to handle multi-classification The labels of the dataset are encoded into binary vectors The class-weight is a dictionary in the format class − label : class − weight Finally, in order to evaluate, we use five most commonly used multiple classification metrics: accuracy, precision, recall, F1 Score (F1) and Area under the ROC Curve (AUC) [27] III PERFORMANCE ANALYSES This section presents a comparison and selection of the six transfer learning models to detect X-Ray images of patients COVID-19 The performance of models is evaluated in four experiments Firstly, the public dataset [22] is trained and tested The new images collected from the local hospital are fused and used to train and test the models in the second experiment Next, we train transfer learning models on the public dataset and test the models on the local dataset Last, the augmented dataset is used for training, and the local dataset is used for testing TABLE I: The number of training and testing subjects used in four experiments Exp Covid Training dataset Pneumonia Normal Covid Testing dataset Pneumonia Normal 460 3418 1266 116 317 855 835 3418 1266 491 317 855 Fig 3: Number of samples per class in the training public dataset A Computing Equipment Experiments are implemented using the Keras library [28] with a TensorFlow [29] This computer use a GPU NVIDIA 1080 TI (16GB Memory) B Experiment setup In order to find the most suitable model for COVID19 prediction on local dataset, we use six popular deep learning architectures including: VGG16 [16], VGG19 [17], DenseNet121 [18], Xception [19], ResNet50 [21], and InceptionV3 [20] These models are trained on ImageNet dataset [26] and using its weights to transfer prediction models We are tuned models by multiple parameters Learning rates are selected between 10−6 to 10−3 The batch-sizes between 16 to 32 were applied The models not use the hidden layer in its architecture Epochs of classification models are set to be 50 C Results and discussions Tables from II to V present classification results of our experiments The maximum values are bolded, while the second values are italicized For the first experiment, the six models are trained on 5144 images of the pubic dataset and evaluated on 1288 the public dataset Table II and Fig present the classification results of these models It is clear that the Densenet121 model has the best overall training technique accuracy of 98.21%, according to the classification results In addition, the Precision, Recall, F1 and AUC of this model also outperform other transfer learning models TABLE II: Performance of Experiment on the public dataset Model Accuracy Precision Recall F1 AUC 94.1 94.17 94.1 93.99 95.62 460 3418 1266 375 317 855 VGG16 835 3418 1266 375 317 855 VGG19 96.27 96.27 96.27 96.27 96.69 Densenet121 98.21 98.22 98.21 98.2 98.83 Xception 97.2 97.22 97.2 97.21 97.45 ResNet50 95.65 95.69 95.65 95.61 97.25 InceptionV3 97.98 97.98 97.98 97.97 98.67 In detail, Table I presents the number samples per class used for training and tesing Fig visualizes the data imbalance issue of dataset This problem is addressed by using ”classweight” setting in Session II 400 2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Fig 4: Comparison of classifying metrics on Experiment Fig 5: Comparison of classifying metrics on Experiment For the second experiment, we use 750 images of the local dataset that is collected at An Giang Regional General Hospital These images are splitted two parts: 375 images are added to the public training dataset and 375 images are appended to the public testing dataset Consequently, the augmented dataset is combined the local and public dataset In this experiment, the transfer learning models are trained and evaluated on the augmented dataset The classification results are shown in Table III and Fig For the DensenNet121 model also has accuracy better other classifiers with an accuracy of 98.62% Table II and III show that the accuracy rises from 98.21% to 98.62% when the testing is carried out using the locally collected COVID-19 X-Ray images For other evaluation metrics, this model provides the best results with a precision of 98.64%, a recall of 98.62%, a F1 score of 98.6 and AUC value of 99.18% Moreover, the local images enrich the current public dataset and improve the performance prediction of models In detail, VGG16 model improves accuracy from 94,1% to 97,65% as well as the accuracy of VGG19 enhances from 96,27% to 97,65% The performance of the ResNet50 model on the augmented dataset improved the accuracy of 2.49% percent points on the public dataset However, for Xception and InceptionV3 models, the accuracy dropped slightly to 95,91% and 97,96% It indicates minor discrepancies between the photos from the two datasets public dataset and evaluated on the local dataset The local dataset has 375 COVID-19, 317 pneumonia and 855 samples Table IV and Fig show the performance comparison of six models In this experiment, the DenseNet121model still has the best performance (Accuracy: 98,45%, Precision: 98,45%, Recall: 98,45%, F1:98,45% and AUC:98,44%) compared with other models TABLE IV: Performance of Experiment on the public training and local testing datasets Model Accuracy Precision Recall F1 AUC VGG16 94.38 94.41 94.38 94.29 95.92 VGG19 96.25 96.26 96.25 96.25 96.7 Densenet121 98.45 98.45 98.45 98.45 98.44 Xception 97.8 97.82 97.8 97.81 97.87 ResNet50 96.32 96.34 96.32 96.28 97.51 InceptionV3 96.12 96.15 96.12 96.13 96.51 TABLE III: Performance of Experiment on the augmented dataset Model Name Accuracy Precision Recall F1 AUC VGG16 97.65 97.65 97.65 97.64 98.26 VGG19 97.65 97.66 97.65 97.63 98.38 Densenet121 98.62 98.64 98.62 98.6 99.18 Xception 95.91 95.9 95.91 95.85 97.03 ResNet50 98.14 98.13 98.14 98.12 98.64 InceptionV3 97.96 97.99 97.96 97.97 98.01 Fig 6: Comparison of classifying metrics on Experiment For the third experiment, six models are trained on the For the last experiment, the models are trained on augmented dataset and evaluated on local dataset The performance comparison of six models are showed in Table V and 401 2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Fig Table IV shows that the DenseNet121model still has the best performance (Accuracy: 98,51%, Precision: 98,54%, Recall: 98,51%, F1: 98,05% and AUC: 99,15%) compared with other models It is clear that the Densent121 model improves the performance classification compared with results of Table IV The results show that the enriched data of the augmented dataset lead to an increase of the performance of the Densenet121 model TABLE V: Performance of Experiment on the augmented training and the local testing datasets Model Name In addition, transfer learning approaches training on new data is faster than starting from scratch For the training time of models on the fused dataset, the DenseNet121 model is trained in 35 minutes The VGG16 and VGG19 are learned in 33 and 39 minutes The Xception model needs more time in 62 minutes Significantly, the ResNet50 model needs only 30 minutes for training Overall, the transfer learning using Densenet121 architecture could performing ultra well with the chest X-Ray images These Fig 4, 5, and show that the DensenNet121 (third group columns) has not only the best accuracy (blue column) but also others metric including precision (red column), recall (yellow column), F1 (green column) and AUC (orange column) Accuracy Precision Recall F1 AUC VGG16 97.48 97.48 97.48 97.46 98.19 VGG19 97.61 97.62 97.61 97.58 98.43 Densenet121 98.51 98.54 98.51 98.05 99.15 Xception 95.99 95.99 95.99 95.94 97.18 IV COMPARED WITH RELATED WORKS 98 97.99 98 97.98 98.58 97.8 97.84 97.8 97.81 97.92 In the proposed study, six models Xception [19], InceptionV2 [20], ResNet50 [21], VGG16 [16], VGG19 [17], and DenseNet121 [18] are used Moreover, 850 new chest X-Ray images of patients as positive COVID is used to enhance data In addition, in order to compared with with the related studies, Table VII presents the comparison of the proposed technique with the studies in the literature using chest X-Ray images to detect COVID-19 ResNet50 InceptionV3 TABLE VII: Comparison of studies for detecting COVID-19 [30] Fig 7: Comparison of classifying metrics on Experiment Table VI shows the corresponding confusion matrices for the DenseNet121 model These tables demonstrate that the Densenet121 model should be selected to predict COVID-19 because of its perfect prediction results TABLE VI: Confusion matrix of DenseNet121 model in the Experiment and (a) Experiment COVID-19 NORMAL PNEUMONIA COVID-19 374 NORMAL 311 PNEUMONIA 17 838 (b) Experiment COVID-19 NORMAL PNEUMONIA COVID-19 375 0 NORMAL 315 PNEUMONIA 21 834 Paper Chest X-Ray COVID-19 images Accuracy Singh et al [31] 50, Cohen Dataset 94.7% Sahinbas et al [32] 50, Cohen Dataset 80% Narin et al [33] 341, Cohen Dataset 96.1% Minaee et al [34] 71, Cohen Dataset – Maguolo et al [35] 144, Cohen Dataset – Hemdan et al [36] 25, Cohen Dataset 90% Khasawneh et al [30] 368, Cohen Dataset 99% Our work 850 new images are fused up to 98.5% V CONCLUSION In conclusion, we investigated transfer-learning approach for COVID-19 prediction using X-ray images in this work The results show that the COVID-19 prediction is improved of current limited datasets by the addition of training images Experiments demonstrade that the DensenNet121 model has achieved to detect the COVID-19 automatically from chest X-Ray by training it with X-Ray images collected from both COVID-19, regular pneumonia patients, and people with normal chest X-Ray images Moreover, the outcomes demonstrate that the this model outperformed amongst the others with the highest accuracy, precision, recall, F1-scores, and AUC of 98.51%, 98.54%, 98.51%, 98.05% and 99.15%, respectively In the future, we will develop federated learning model for predicting clinical outcomes in patients with COVID19 as well as using computer vision algorithms to improve performance 402 2021 8th NAFOSTED Conference on Information and Computer Science (NICS) ACKNOWLEDGMENT The authors gratefully acknowledge An Giang Regional General Hospital for providing access to Chest X-Ray images in this research R EFERENCES [1] H Ritchie and et al., “Coronavirus pandemic (covid-19),” Our World in Data, 2020, https://ourworldindata.org/coronavirus [2] H.-Y Wang, X.-L Li, Z.-R Yan, X.-P Sun, J Han, and B.-W Zhang, “Potential neurological symptoms of covid-19,” Therapeutic advances in neurological disorders, vol 13, p 1756286420917830, 2020 [3] A Alharthy, M Abuhamdah, A Balhamar, F Faqihi, N Nasim, S Ahmad, A Noor, H Tamim, S A Alqahtani, A A A S B Abdulaziz Al Saud et al., “Residual lung injury in patients recovering from covid-19 critical illness: A prospective longitudinal point-of-care lung ultrasound study,” 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pneumonia patients, and people with normal chest X-Ray images Moreover,... CONCLUSION In conclusion, we investigated transfer- learning approach for COVID-19 prediction using X-ray images in this work The results show that the COVID-19 prediction is improved of current limited... Predicting covid-19 from chest x-ray images using deep transfer learning, ” Medical image analysis, vol 65, p 101794, 2020 [35] G Maguolo and L Nanni, “A critic evaluation of methods for covid-19