Investigating the Robustness of Pre-trained Networks on OCT-Dataset (original) (raw)
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medRxiv (Cold Spring Harbor Laboratory), 2024
Vision loss due to chronic-degenerative diseases is a primary cause of blindness worldwide. Deep learning architectures utilizing optical coherence tomography images have proven effective for the early diagnosis of ocular pathologies. Nevertheless, most studies have emphasized the best outcomes using optimal hyperparameter combinations and extensive data availability. This focus has eclipsed the exploration of how model learning capacity varies with different data volumes. The current study evaluates the learning capabilities of efficient deep-learning classification models across various data amounts, aiming to determine the necessary data portion for effective clinical trial classifications of ocular pathologies. A comprehensive review was conducted, which included 295 papers that employed OCT images to classify one or more of the following retinal pathologies: Drusen, Diabetic Macular Edema, and Choroidal Neovascularization. Performance metrics and dataset details were extracted from these studies. Four Convolutional Neural Networks were selected and trained using three strategies: initializing with random weights, fine-tuning, and retraining only the classification layers. The resultant performance was compared based on training size and strategy to identify the optimal combination of model size, dataset size, and training approach. The findings revealed that, among the models trained with various strategies and data volumes, three achieved 99.9% accuracy, precision, recall, and F1 score. Two of these models were fine-tuned, and one used random weight initialization. Remarkably, two models reached 99% accuracy using only 10% of the original training dataset. Additionally, a model that was less than 10% the size of the others achieved 98.7% accuracy and an F1 score on the test set while requiring 100 times less computing time. This study is the first to assess the impact of training data size and model complexity on performance metrics across three scenarios: random weights initialization, fine-tuning, and retraining classification layers only, specifically utilizing optical coherence tomography images. .
Deep learning-based classification of eye diseases using Convolutional Neural Network for OCT images
Deep learning-based classification of eye diseases using Convolutional Neural Network for OCT images, 2024
Deep learning shows promising results in extracting useful information from medical images. The proposed work applies a Convolutional Neural Network (CNN) on retinal images to extract features that allow early detection of ophthalmic diseases. Early disease diagnosis is critical to retinal treatment. Any damage that occurs to retinal tissues that cannot be recovered can result in permanent degradation or even complete loss of sight. The proposed deep-learning algorithm detects three di erent diseases from features extracted from Optical Coherence Tomography (OCT) images. The deeplearning algorithm uses CNN to classify OCT images into four categories. The four categories are Normal retina, Diabetic Macular Edema (DME), Choroidal Neovascular Membranes (CNM), and Age-related Macular Degeneration (AMD). The proposed work uses publicly available OCT retinal images as a dataset. The experimental results show significant enhancement in classification accuracy while detecting the features of the three listed diseases.
ClaRet -- A CNN Architecture for Optical Coherence Tomography
Cornell University - arXiv, 2022
Optical Coherence Tomography is a technique used to scan the Retina of the eye and check for tears. In this paper, we develop a Convolutional Neural Network Architecture for OCT scan classification. The model is trained to detect Retinal tears from an OCT scan and classify the type of tear. We designed a block-based approach to accompany a pre-trained VGG-19 using Transfer Learning by writing customised layers in blocks for better feature extraction. The approach achieved substantially better results than the baseline we initially started out with.
Comparisonal study of Deep Learning approaches on Retinal OCT Image
ArXiv, 2019
In medical science, the use of computer science in disease detection and diagnosis is gaining popularity. Previously, the detection of disease used to take a significant amount of time and was less reliable. Machine learning (ML) techniques employed in recent biomedical researches are making revolutionary changes by gaining higher accuracy with more concise timing. At present, it is even possible to automatically detect diseases from the scanned images with the help of ML. In this research, we have taken such an attempt to detect retinal diseases from optical coherence tomography (OCT) X-ray images. Here, we propose a deep learning (DL) based approach in detecting retinal diseases from OCT images which can identify three conditions of the retina. Four different models used in this approach are compared with each other. On the test set, the detection accuracy is 98.00\% for a vanilla convolutional neural network (CNN) model, 99.07\% for Xception model, 97.00\% for ResNet50 model, and...
CNN Classification of Multi-Scale Ensemble OCT for Macular Image Analysis
IJEER , 2022
Computer-Aided Diagnosis (CAD) of retinal pathology is a dynamic medical analysis area. The CAD system in the optical coherence tomography (OCT) is important for the monitoring of ocular diseases because of the heavy utilization of the retinal OCT imaging process. The Multi-Scale Expert Convolution Mixture (MCME) is designed to classify the normal retina. OCT is becoming one of the most popular non-invasive evaluation approaches for retinal eye disease. The amount of OCT is growing and the automation of OCT image analysis is becoming increasingly necessary. The surrogate-aided classification approach is to automatically classify retinal OCT images because of the Convolution Neural Network (CNN). The methods to classify OCT images and macular OCT classification are done by using CNN. Maculopathy is a combined collection of diseases to facilitate the effect of the inner region of the retina identified as the macula. Central Serous Choric Retinopathy (CSCR) and macular edema are the main two types of maculopathies. Numerous researches have focused on the detection of these macular disorders with OCT. It is used to overcome retinal diseases.
Retinal Disease Classification from OCT Images Using Deep Learning Algorithms
2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2021
Optical Coherence Tomography (OCT) is a noninvasive test that takes cross-section pictures of the retina layer of the eye and allows ophthalmologists to diagnose based on the retina's layers. Therefore, it is an important modality for the detection and quantification of retinal diseases and retinal abnormalities. Since OCT provides several images for each patient, it is a time consuming work for ophthalmologists to analyze the images. This paper proposes deep learning models that categorize patients' OCT images into four categories such as Choroidal neovascularization (CNV), Diabetic macular edema (DME), Drusen, and Normal. Two different models are proposed. One is using three binary Convolutional Neural Network (CNN) classifiers and the other is using four binary CNN classifiers. Several CNNs, such as VGG16, VGG19, ResNet50, ResNet152, DenseNet121, and InceptionV3, are adapted as feature extractors to develop the binary classifiers. Among them, the proposed model using VGG16 for CNV vs. Other classes, VGG16 for DME vs. other classes, VGG19 for Drusen vs. Other classes, and InceptionV3 for Normal vs. other classes shows the best performance with 0.987 accuracy, 0.987 sensitivity, and 0.996 specificity. The binary classifier for Normal class has 0.999 accuracy. These results show their potential to work as a second reader for ophthalmologists.
Entropy
Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM)...
Journal of Ophthalmology
Purpose. Although optical coherence tomography (OCT) is essential for ophthalmologists, reading of findings requires expertise. The purpose of this study is to test deep learning with image augmentation for automated detection of chorioretinal diseases. Methods. A retina specialist diagnosed 1,200 OCT images. The diagnoses involved normal eyes (n=570) and those with wet age-related macular degeneration (AMD) (n=136), diabetic retinopathy (DR) (n=104), epiretinal membranes (ERMs) (n=90), and another 19 diseases. Among them, 1,100 images were used for deep learning training, augmented to 59,400 by horizontal flipping, rotation, and translation. The remaining 100 images were used to evaluate the trained convolutional neural network (CNN) model. Results. Automated disease detection showed that the first candidate disease corresponded to the doctor’s decision in 83 (83%) images and the second candidate disease in seven (7%) images. The precision and recall of the CNN model were 0.85 and ...
Machine learning in image analysis in ophthalmology
einstein (São Paulo), 2021
Editorial: Macular degeneration is the leading cause of irreversible blindness in developed countries in individuals aged over 50 years.(1) Aiming to better diagnose and monitor the disease, algorithms have been developed to detect lesions in optical coherence tomography (OCT). The ability of machine learning algorithms to detect OCT lesions may already be comparable to that of retina specialists.(2)
Bias Assessment in Medical Imaging Analysis: A Case Study on Retinal OCT Image Classification
Proceedings of the 14th International Conference on Agents and Artificial Intelligence
Deep learning classifiers can achieve high accuracy in many medical imaging analysis problems. However, when evaluating images from outside the training distribution-e.g., from new patients or generated by different medical equipment-their performance is often hindered, highlighting that they might have learned specific characteristics and biases of the training set and can not generalize to real-world scenarios. In this work, we discuss how Transfer Learning, the standard training technique employed in most visual medical tasks in the literature, coupled with small and poorly collected datasets, can induce the model to capture such biases and data collection artifacts. We use the classification of eye diseases from retinal OCT images as the backdrop for our discussion, evaluating several well-established convolutional neural network architectures for this problem. Our experiments showed that models can achieve high accuracy in this problem, yet when we interpret their decisions and learned features, they often pay attention to regions of the images unrelated to diseases.