Image denoising plays a crucial role as a preprocessing step in the analysis of medical images. Over the past three decades, various algorithms have been proposed, each exhibiting different denoising performances. More recently, deep learning-based models have demonstrated remarkable success, surpassing traditional methods. However, these advanced models face limitations, particularly in terms of demanding large training sample sizes and incurring high computational costs. In this paper, we address these challenges and propose a novel approach utilizing denoising autoencoders constructed with convolutional layers. Our method is distinctive in its ability to efficiently denoise medical images even when trained on a small sample size. We demonstrate the effectiveness of our approach by combining heterogeneous images, thereby boosting the effective sample size and subsequently enhancing denoising performance. One notable advantage of our method is its adaptability to the complexities of medical imaging. Even with the simplest network architectures, our denoising autoencoders exhibit an exceptional ability to reconstruct images, even in scenarios where corruption levels are so high that noise and signal become indistinguishable to the human eye. Through this research, we aim to provide a practical and effective solution for medical image denoising that overcomes the limitations associated with large training datasets and computational resource requirements commonly observed in deep learning-based models.
Introduction
Overview
1 Introduction to Digital Image Processing
Image processing is a significant field in computer science and information technology, primarily focusing on the processing, transformation, and understanding of information from images and videos The main goal of image processing is to extract useful information from images and videos, making them easy to read, analyze, or use in various applications
In the realm of digital image processing, the field concentrates on two major tasks:
• Improvement of pictorial information for human interpretation
• Processing of image data for storage, transmission, and representation for autonomous machine perception
These tasks involve applying methods and algorithms to enhance image quality, such as increasing resolution, improving contrast, noise reduction, and various other processing tasks to make image information clearer and more manageable Concurrently, image processing also involves the analysis and extraction of feature information from images, aiding computers in understanding the content of images and videos automatically
The continuum from image processing to computer vision can be broken up into low-, mid- and high-level processes
Picture 1: Levels of Image Processing
There are some Key Stages in Digital Image Processing:
Picture 2: Key Stages in Digital Image Processing
In this project, I am conducting research on Image Denoising, within the context of Image Enhancement, is a facet that focuses on minimizing or eliminating noise from an image
2 Differences between Image Processing and Computer Vision
Attribute Image Processing Computer Vision
A field of computer science and technology focusing on the processing, transformation, and understanding of information from images and videos
A branch of artificial intelligence aimed at enabling computers to understand and solve visual tasks similar to humans
Output Processed images (enhanced, filtered, etc.)
Information or decisions based on the recognition and understanding of visual content
- Improving image quality (resolution, contrast)
- Removing noise from images (Image Denoising)
- Face recognition in images or videos
- Object classification in images (animals, objects)
Picture 3: Differences between Image Processing & Computer Vision
3 The history of image processing
The history of image processing began with early efforts to understand and utilize images, and it has undergone significant advancements over the decades Below is a summary of the development history of image processing: a) Early Foundations (1950s - 1960s):
During this period, research focused on developing basic image processing methods for generating and representing images
Some of the initial works, such as digital image processing, were conducted on analog computers b) Preliminary Image Processing Era (1970s - 1980s):
The advent of digital computers opened up the possibility of digital image processing and the implementation of more complex algorithms
Methods like low-pass filtering and high-pass filtering were developed to enhance image quality c) Breakthroughs in Computer Vision (1980s - 1990s):
Computer vision began to emerge as an independent field, concentrating on image recognition and understanding
Algorithms such as Hough Transform, Edge Detection, and Segmentations appeared, paving the way for new applications d) Advancements in Statistical Methods and Machine Learning (2000s - Present):
The prevalence of statistical methods and machine learning changed the landscape of image processing, with the emergence of deep learning models
Convolutional Neural Networks (CNNs) demonstrated outstanding performance in various computer vision tasks, from object recognition to medical image processing e) Wide-Spread Applications and Future Prospects (Present - Ongoing):
Image processing has become a crucial component in various fields, including healthcare, autonomous vehicles, security, and many others
Ongoing research continues to focus on developing advanced image processing methods and implementing them in real-world applications
Image denoising plays a crucial role as a preprocessing step in the analysis of medical images Over the past three decades, various algorithms have been proposed, each exhibiting different denoising performances More recently, deep learning-based models have demonstrated remarkable success, surpassing traditional methods However, these advanced models face limitations, particularly in terms of demanding large training sample sizes and incurring high computational costs
In this paper, we address these challenges and propose a novel approach utilizing denoising autoencoders constructed with convolutional layers Our method is distinctive in its ability to efficiently denoise medical images even when trained on a small sample size
We demonstrate the effectiveness of our approach by combining heterogeneous images, thereby boosting the effective sample size and subsequently enhancing denoising performance
One notable advantage of our method is its adaptability to the complexities of medical imaging Even with the simplest network architectures, our denoising autoencoders exhibit an exceptional ability to reconstruct images, even in scenarios where corruption levels are so high that noise and signal become indistinguishable to the human eye
Through this research, we aim to provide a practical and effective solution for medical image denoising that overcomes the limitations associated with large training datasets and computational resource requirements commonly observed in deep learning-based models
In the realm of medical imaging, achieving precise diagnoses and conducting thorough analyses of medical images necessitate a high level of accuracy and detail Unfortunately, these images are frequently marred by noise originating from diverse factors Addressing this significant challenge has become imperative, and I am motivated to leverage the capabilities of deep learning to tackle this issue
The inherent complexity and intricacy of medical images demand sophisticated solutions for effective denoising By harnessing the power of autoencoders, a type of neural network, I aim to develop a robust and efficient method for image denoising in medical imaging Autoencoders have demonstrated remarkable capabilities in learning complex patterns and representations, making them well-suited for handling the intricate nature of medical image data
My motivation stems from the realization that a successful image denoising approach can substantially enhance the accuracy of diagnostic procedures and contribute to more reliable medical analyses Through the utilization of autoencoders, I aspire to bring about advancements in the field, ultimately leading to improved image quality and facilitating more accurate medical assessments
The landscape of image denoising techniques has witnessed significant advancements, with various approaches demonstrating remarkable capabilities in addressing noise-related challenges Notably, BM3D has been regarded as state-of-the-art in image denoising, characterized by its well-engineered methodology However, Burger et al challenged this notion by showcasing that a simple multi-layer perceptron (MLP) can achieve denoising performance comparable to BM3D
Picture 4: (“Image denoising: Can plain neural networks compete with BM3D?.” (CVPR), 2012)
An addition to the image denoising is the introduction of denoising autoencoders Serving as fundamental components for deep networks, these autoencoders, as extended by Vincent et al., offer a novel approach to image denoising The concept involves stacking denoising autoencoders to construct deep networks, where the output of one denoising autoencoder is fed as input to the subsequent layer
Jain et al proposed image denoising using convolutional neural networks (CNNs), demonstrating that even with a small sample of training images, performance on par or superior to state-of-the-art methods based on wavelets and Markov random fields can be achieved Additionally, Xie et al leveraged stacked sparse autoencoders for both image denoising and inpainting, showcasing performance comparable to K-SVD
Agostenelli et al explored the application of adaptive multi-column deep neural networks for image denoising, constructed through a combination of stacked sparse autoencoders This innovative system demonstrated robustness across various noise types The collective findings from these related works highlight the versatility and effectiveness of different autoencoder-based approaches, including MLPs, convolutional neural networks, and stacked sparse autoencoders, in the domain of image denoising.
Problem Identification
The primary input of problem is a grayscale image that are affected by noise Noise can be introduced during the image acquisition process or due to other environmental factors
-> The desired output is a denoised image where the unwanted noise has been effectively removed while preserving the essential features and details in the images
3 Constraints: a) Limited Training Data: Constraints on the size of the training dataset may limit the number of available images for model training This can be a challenge, especially in medical data where images are scarce b) Model Depth: Constraints on the number of layers in the autoencoder can be applied to control the complexity of the model and mitigate the risk of overfitting c) Model Stability: Ensuring that the model is not overly complex to avoid overfitting and maintain stable performance on new data d) Acceptable Training Time: Limiting the training time of the model can be an important constraint, especially when computational resources are limited e) Optimal Performance: Constraints on the model's performance, particularly achieving high denoising performance on various types of noise and different lighting conditions f) Flexibility: The model needs to be flexible and adaptable to various types of medical images and imaging conditions g) Noise Tolerance: The model should have the ability to handle and denoise images in noisy conditions without losing important information h) Scalability: The model should be scalable to apply to a large volume of diverse medical images without requiring extensive re-tuning
4 Requirments: a) High Denoising Accuracy: The autoencoder should demonstrate high accuracy in denoising images, effectively removing noise while preserving essential details b) Adaptability to Various Image Types: The model should be able to adapt to different types of medical images, considering variations in imaging modalities and structures c) Efficient Handling of Limited Training Data: The autoencoder should be designed to effectively learn from a limited dataset, considering potential constraints on the availability of labeled training images d) Flexibility in Model Architecture: The architecture of the autoencoder should be flexible, allowing adjustments to the number of layers and neurons to achieve optimal denoising performance e) Robustness to Varied Lighting Conditions: The autoencoder should be robust to changes in lighting conditions, providing consistent denoising performance across different levels of illumination f) Interpretability of Results: The denoising results should be interpretable and should not introduce artifacts or distortions that could mislead medical professionals during image analysis g) Scalability: The model should be scalable to handle a growing dataset and potential advancements in imaging technology without requiring significant reconfiguration h) Compatibility with Existing Infrastructure: The integration of the autoencoder into existing medical imaging systems or workflows should be seamless and compatible with established infrastructure i) User-Friendly Interface for Training and Evaluation: If applicable, there should be a user-friendly interface for training the autoencoder and evaluating its denoising performance, making it accessible to practitioners without extensive machine learning expertise.
PRELIMINARIES
Noisy image
A " Noisy image " refers to an image that has been corrupted, containing unwanted or random components added from various sources Noise can appear in an image due to various factors such as poor lighting conditions, inaccurate sensor equipment, or imperfect data transmission processes
Where is the noisy image produced as a sum of original image x and some noise z Images with noise often appear blurry and unclear, reducing the quality of the original image and posing challenges in analyzing and interpreting information within the image The goal of the Image Denoising problem is to utilize an Autoencoder model to eliminate or minimize these noisy components, reconstructing the original image with improved quality while retaining essential information
All denoising methods try to approximate x using x as close as possible.
Denoising
Denoising image, the process of removing noise or unwanted artifacts from images, plays a crucial role in various scientific fields In particular, Medical Image Denoising is of paramount importance due to its direct impact on diagnostic accuracy and the overall reliability of medical imaging
In scientific research, especially in disciplines such as astronomy, biology, and materials science, images captured through various instruments are often contaminated with noise This noise can obscure critical details and affect the accuracy of subsequent analyses Addressing these challenges requires advanced denoising techniques capable of preserving essential features while eliminating unwanted distortions
3) Significance of Medical Image Denoising:
In the medical field, where image quality directly influences diagnostic decisions, Medical Image Denoising is a critical step in enhancing the clarity and precision of medical imaging modalities such as X-rays, CT scans, MRIs, and ultrasound The importance of this process extends to various medical applications, including disease detection, treatment planning, and surgical guidance
4) Impact on Diagnosis and Treatment:
- Enhanced Visibility: Denoising ensures that medical images are free from artifacts, allowing healthcare professionals to have a clearer view of anatomical structures and abnormalities
- Improved Diagnostic Accuracy: Clean and precise images contribute to accurate diagnosis, reducing the likelihood of misinterpretations or missed diagnoses
- Optimized Treatment Planning: Medical Image Denoising aids in the planning of surgical procedures, radiation therapy, and other interventions by providing clinicians with high- quality images for detailed analysis
Recent advancements in denoising techniques, particularly the application of deep learning algorithms such as convolutional neural networks (CNNs) and autoencoders, have significantly improved the efficacy of image denoising These methods can adaptively learn complex patterns in medical images, making them valuable tools in the pursuit of high-quality diagnostic imaging.
Autoencoder
An autoencoder is a type of neural network that tries to learn an approximation to identity function using backpropagation, given a set of unlabeled training inputs x (1) , x (2) , , x (n ) , it uses z (i) = x (i)
An autoencoder first takes an input x ∈ [0,1] d and maps(encode) it to a hidden representation y ∈ [0, 1] d′ using deterministic mapping, such as y = s(W x + b) where s can be any non linear function Latent representation y is then mapped back(decode) into a reconstruction z, which is of same shape as x using similar mapping z = s(W′y + b′)Model parameters (W,W′,b,b′) are optimized to minimize reconstruction error prime symbol is not a matrix transpose Model parameters (W,W′,b,b′) are optimized to minimize recon- struction error, which can be assessed using different loss functions such as squared error or cross-entropy.
Layer L1 is input which is encoded in layer L2 using latent representation and input is reconstructed at L3 Using number of hidden units lower than inputs forces autoencoder to learn a compressed approximation Mostly an autoencoder learns low dimensional representation very similar to Principal Component Analysis (PCA) Having hidden units larger than number of inputs can still discover useful insights by imposing certain sparsity constraints.
Denoising Autoencoder
Denoising autoencoder is a stochastic extension to classic autoencoder, that is we force the model to learn reconstruction of input given its noisy version A stochastic corruption process randomly sets some of the inputs to zero, forcing denoising autoencoder to predict missing(corrupted) values for randomly selected subsets of missing patterns
Denoising autoencoders can be stacked to create a deep network (stacked denoising autoencoder)
Output from the layer below is fed to the current layer and training is done layer wise.
Convolutional Autoencoder
Convolutional autoencoders are based on standard autoencoder architecture with convolutional encoding and decoding layers Compared to classic autoencoders, convolutional autoencoders are better suited for image processing as they utilize full capability of convolutional neural networks to exploit image structure
In convolutional autoencoders, weights are shared among all input locations which helps preserve local spatiality Rep- resentation of ith feature map is given as where bias is broadcasted to whole map, ∗ denotes convo- lution (2D) and s is an activation Single bias per latent map is used and reconstruction is obtained as where c is bias per input channel, H is group of latent feature maps, W ̃ is flip operation over both weight dimensions
Backpropogation is used for computation of gradient of the error function with respect to the parameters.
Methodology
Data Preprocessing
Collect a dataset of clean images to serve as the foundation for training the Autoencoder
2 Resizing Images for Model Input
To ensure that both clean and noisy images; images from another dataset are appropriately sized for input into the Autoencoder model, we need to perform the image resizing process This process involves adjusting the resolution of the images to match the requirements of the model
Apply different types and levels of noise to the clean images to generate a diverse set of noisy images This step is crucial for training the Autoencoder to handle various noise patterns
Picture 12:adding noise to data
Model Architecture
Implement an Autoencoder architecture consisting of an encoder and a decoder The encoder compresses the input image into a latent representation, and the decoder reconstructs the clean image from this representation
Define a suitable loss function for training the Autoencoder Mean Squared Error (MSE) can be employed, comparing the reconstructed clean image with the original clean image.
Training
Divide the dataset into training and validation sets The training set comprises paired clean-noisy images, while the validation set includes clean images for evaluating the model's performance
Train the Autoencoder using the noisy images as input and the corresponding clean images as the target output Optimize the model parameters to minimize the chosen loss function.
Evaluation
Analyze the learned latent representations to ensure that the Autoencoder is capturing meaningful features from the noisy images
Evaluate the performance of the Autoencoder by reconstructing clean images from the noisy ones Compare the reconstructed images with the original clean images to assess the denoising capability of the model
Utilize metrics such as PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) to quantitatively measure the quality of the denoised images.
Experiment and Result
Data
In this project, I use 2 dataset: “Digital Dental Periapical X-Ray Database for Caries
Screening” and “The mini-MIAS database of mammograms”
1 Digital Dental Periapical X-Ray Database for Caries Screening a) General Dataset Description:
- Dataset Name: “Digital Dental Periapical X-Ray Database for Caries Screening”
+ Abdolvahab Ehsani Rad, Mohd Shafry Mohd Rahim, Amjad Rehman, and Tanzila Saba Digital dental x-ray database for caries screening 3D Research, 7(2):1–5, 2016
+ Rad, A E., Mohd Rahim, M S., Rehman, A., Altameem, A., & Saba, T (2013) Evaluation of current dental radiographs segmentation approaches in computer-aided applications IETE Technical Review, 30(3), 210-222
+ Abdolvahab Ehsani Rad, Mohd Shafry Mohd Rahim, Hoshang Kolivand, and Ismail Bin Mat Amin Morphological region-based initial contour algorithm for level set methods in image segmentation Multimedia Tools and Applications, pages 1–17, 2016
+ Rad, A E., Amin, I B M., Rahim, M S M., & Kolivand, H (2015) Computer-Aided Dental Caries Detection System from X-Ray Images In Computational Intelligence in Information Systems (pp 233-243) Springer International Publishing b) Technical Specifications:
- Size: The dataset consists of 120 images
- Data Format: images in jpg format
- Size of Each Sample: 120 image with (Width, Heigh, Chanels) = (748, 512, 3) c) Terms of Use:
It is free to use the database in scientific research but must abide by the licence agreement when using the imagery d) Origin and References:
- Origin: Indicate where the data was sourced from
- References: https://mynotebook.labarchives.com/share/Vahab/MjAuOHw4NTc2Mi8xNi9UcmVlTm9 kZS83NzM5OTk2MDZ8NTIuOA= Picture 13: "Digital Dental Periapical X-Ray Database for Caries Screening"
2 The mini-MIAS database of mammograms a) General Dataset Description:
- Dataset Name: “The mini-MIAS database of mammograms”
+ Truth-Data: C R M Boggis and I Hutt
+ Co-Workers: S Astley, D Betal, N Cerneaz, D R Dance, S-L Kok, J Parker, I Ricketts,
J Savage, E Stamatakis and P Taylor b) Dataset Objectives:
Purpose of Data: The Mini-MIAS dataset primarily focuses on mammogram images, which are close-up images of the breast region used to detect signs of breast pathology, including breast cancer c) Technical Specifications:
- Size: The dataset consists of 322 images
- Data Format: images in pgm format
- Size of Each Sample: 322 image with (Width, Heigh, Chanels) = (1024, 1024, 3) The images have been centered in the matrix
- Details: The follow list gives the films in the MIAS database and provides appropriate details as follows:
+ 1st column: MIAS database reference number
+ 2nd column: Character of background tissue:
+ 3rd column: Class of abnormality present:
CIRC -> Well-defined/circumscribed masses
MISC -> Other, ill-defined masses
+ 4th column: Severity of abnormality;
+ 5th, 6th columns: x,y image-coordinates of centre of abnormality
+ 7th column: Approximate radius (in pixels) of a circle enclosing the abnormality d) Ownership and Terms of Use:
- Terms of Use: It is free to use the database in scientific research but must abide by the licence agreement when using the imagery e) Origin and References:
- Origin: Indicate where the data was sourced from
- References: Ihttp://peipa.essex.ac.uk/info/mias.html
Picture 14: The mini-MIAS database of mammograms
Choosing Hyperparameters for Model Training
- Decision: Set epochs = 1000 and implement EarlyStopping from tensorflow.keras.callbacks to automatically halt training
+ By choosing a relatively large number of epochs (1000), the model has ample opportunities to learn the denoising patterns from the data
+ EarlyStopping is employed to monitor the validation loss and stop training if it doesn't improve for a certain number of consecutive epochs (patience = 10), preventing overfitting
+ A smaller batch size of 10 is chosen to reduce memory requirements and speed up the training process
+ It strikes a balance between computational efficiency and model convergence
- Decision: Use the default learning rate (0.001)
+ The default learning rate is often a reasonable starting point, especially when using popular optimizers like Adam
+ Fine-tuning the learning rate may be necessary during experimentation, but the default is generally effective for various tasks
- Decision: Use the Adam optimizer
+ Adam is a widely used optimizer known for its efficiency and adaptability across different types of neural networks
+ It combines the advantages of both AdaGrad and RMSProp, making it suitable for various scenarios
- Decision: Set loss = mean_squared_error
+ Mean Squared Error (MSE) is commonly used for image reconstruction tasks, including image denoising
+ It measures the average squared difference between the predicted and actual pixel values, aligning with the goal of minimizing reconstruction error
+ Two convolutional layers with max-pooling to capture hierarchical features and reduce spatial dimensions
+ Two convolutional layers with upsampling to reconstruct the denoised image
+ The final layer uses the sigmoid activation function to output pixel values between 0 and 1
+ Training Device: Utilizing GPU ('/device:GPU:0') for faster training
+ Early Stopping: Implementing early stopping to prevent overfitting and achieve optimal performance.
Metrics
1 Peak Signal-to-Noise Ratio (PSNR):
Peak Signal-to-Noise Ratio (PSNR) is a widely used metric for evaluating the quality of denoised images It measures the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation In the context of image denoising, PSNR quantifies how well the denoised image preserves the original image details
Explanation: PSNR measures the similarity between the denoised image and the original image based on the level of noise (MSE) A higher PSNR often compensates for information loss during denoising
Interpretation: A higher PSNR value indicates a lower level of noise and better preservation of image quality It is expressed in decibels (dB), and a higher PSNR value implies a higher similarity between the denoised image and the original clean image
Structural Similarity Index (SSIM) is a perceptual metric that evaluates the structural information content, luminance, and contrast of images Unlike PSNR, SSIM considers human visual perception in its assessment, making it more aligned with the way humans perceive image quality
+ SSIM compares pixel windows in the denoised and original images based on structural similarity, contrast similarity, and luminance similarity
+ SSIM results range from [−1,1] with 1 indicating perfect similarity
The SSIM index ranges from -1 to 1, where 1 indicates perfect similarity between the denoised and original images A higher SSIM value signifies better preservation of structural information, luminance, and contrast
3 Why Choose PSNR and SSIM: a) Complementary Information: PSNR and SSIM provide complementary insights into the performance of an image denoising model PSNR focuses on pixel-wise differences, while SSIM incorporates perceptual aspects b) Perceptual Quality: SSIM accounts for human visual perception, making it valuable in scenarios where the goal is to improve the perceptual quality of denoised images c) Commonly Used Benchmarks: PSNR is a standard metric widely used in image processing, while SSIM has gained popularity for its perceptual relevance Using both metrics provides a comprehensive evaluation framework d) Interpretability: PSNR is easy to interpret and is commonly used in literature
SSIM enhances the evaluation by considering structural information and human perception
By utilizing both PSNR and SSIM, we can obtain a more comprehensive understanding of the denoising model's performance, addressing both numerical fidelity and perceptual quality aspects
“When comparing images, the mean squared error (MSE)–while simple to implement–is not highly indicative of perceived similarity Structural similarity aims to address this shortcoming by taking texture into account” (skimage)
Picture 16: Why not choose MSE
Result
In the experimentation phase, the initial attempt involved introducing Gaussian noise to the first half and salt-and-pepper noise to the second half of the "The mini-MIAS database of mammograms." The purpose was to train the autoencoder model to learn denoising strategies for both types of noise simultaneously.
Unfortunately, the outcomes were not promising, indicating that the model struggled to effectively reduce both types of noise simultaneously
Subsequently, the approach was refined by applying Gaussian noise uniformly across the entire dataset This adjustment aimed to simplify the learning task for the autoencoder, focusing on a single type of noise to enhance its capacity for noise reduction This modification was motivated by the understanding that addressing two distinct types of noise concurrently posed challenges for the model
2 Not Augmentation a) On “Digital Dental Periapical X-Ray Database for Caries Screening”
Picture 18: Result on Dental (Not augmentation) b) On “The mini-MIAS database of mammograms”
Picture 19: Result on MIAS (Not augmentation) c) Conclusion
In the experimental phase, it has been observed that while Autoencoder demands a larger amount of memory compared to traditional noise filters such as Mean, Median, Gaussian, and Bilateral, its denoising results are significantly superior Here are some reasons explaining this phenomenon:
+ Autoencoder: With its capability for automatic learning, an autoencoder can understand and autonomously learn denoising techniques based on the complex features of the data This enables it to achieve higher performance when handling non-uniform and complex noise
+ Traditional Filters: Traditional filters typically apply fixed techniques and lack the ability for automatic learning Consequently, they may be ineffective when dealing with complex or diverse noise patterns
+ Autoencoder: It maintains a higher level of detail and the ability to retain crucial features of the image, resulting in denoised images of superior quality
+ Traditional Filters: Traditional filters may lead to information loss and reduced detail, especially when confronted with complex noise patterns
+ Autoencoder: It possesses flexibility in handling various types of noise and can be adjusted to address specific cases
+ Traditional Filters: They may lack the flexibility needed to cope with diverse noise patterns and may struggle to optimize denoising quality in every situation
The process of data augmentation involves creating variations of the original images by applying various transformations such as rotation, flipping, scaling, or changes in brightness and contrast This is a common technique used in machine learning to artificially increase the size of the training dataset, providing the model with more diverse examples to learn from By generating five additional augmented images for each original image in the Train, Validation, and Test sets, you have effectively introduced greater variability and complexity into your dataset This can contribute to a more robust and generalized model, as it learns to recognize patterns across a wider range of conditions a) On “Digital Dental Periapical X-Ray Database for Caries Screening”
Picture 20: Result on Denal (Augmentation) b) On “The mini-MIAS database of mammograms”
Picture 21: Result on MIAS (Augmentation) c) Conclusion
In conclusion, the use of augmentation has proven to enhance results during model training, albeit with the trade-off of increased resource demands While augmentation contributes to diversity and improved dataset quality, it concurrently poses a challenge in terms of heightened RAM requirements.
Demo
In this project, I use gradio for Demo
Gradio is a Python library that facilitates the quick and easy creation of user interfaces for machine learning models Gradio.app is an online service that allows you to share and deploy your machine learning model without requiring in-depth knowledge of web programming or deployment
Here is some useful information about Gradio.app: a) Simple User Interface Creation: Gradio allows you to create a user interface easily using the functions and classes provided by the library b) Support for Various Model Types: Gradio supports multiple types of machine learning models, ranging from traditional machine learning to deep learning You can integrate machine learning models, deep learning models, and various other types of models c) Easy Integration: Gradio.app provides an online web interface builder that allows you to customize your user interface without requiring extensive knowledge of web programming d) Quick Sharing and Deployment: Gradio.app enables you to share your model with others by simply sharing the link to the created interface This makes deployment and sharing with colleagues or the community convenient e) Multi-Language Support: Gradio supports multiple programming languages, providing flexibility in deploying your model f) Customizable Configuration: You can customize interface parameters, such as input and output data types, to suit your specific needs g) Cloud Platform Support: Gradio.app integrates with cloud services such as
Google Colab, making it easy to deploy your model on popular cloud platforms Here is the demo for this Project: