Diabetic Retinopathy Image Classification Using Deep Neural Network

En, Parvathy; G, Bharadwaja Kumar

doi:10.22159/ajpcr.2017.v10s1.20512

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…This study focuses on analyzing classification methods to detect these three pathologies using medical imaging [10] [11]. Recent advances have shown that deep learning algorithms, particularly using optical coherence tomography (OCT) [12], [13], [14] and fundus images [15], [16], can automatically extract pathological features. Furthermore, features identified in one pathology may be applicable to others, allowing for efficient classification across multiple conditions [17].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Ocular Pathology Classification with CNNs and OCT Imaging: A Systematic and Performance Review

Hauri-Rosales,

Pérez,

Garcia-Roa

et al. 2024

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%

Optimizing Ocular Pathology Classification with CNNs and OCT Imaging: A Systematic and Performance Review

Hauri-Rosales,

Pérez,

Garcia-Roa

et al. 2024

Preprint

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Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Dubey

Dixit

2022

Multimed Tools Appl

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Diabetes is a long-term condition in which the pancreas quits producing insulin or the body’s insulin isn’t utilised properly. One of the signs of diabetes is Diabetic Retinopathy. Diabetic retinopathy is the most prevalent type of diabetes, if remains unaddressed, diabetic retinopathy can affect all diabetics and become very serious, raising the chances of blindness. It is a chronic systemic condition that affects up to 80% of patients for more than ten years. Many researchers believe that if diabetes individuals are diagnosed early enough, they can be rescued from the condition in 90% of cases. Diabetes damages the capillaries, which are microscopic blood vessels in the retina. On images, blood vessel damage is usually noticeable. Therefore, in this study, several traditional, as well as deep learning-based approaches, are reviewed for the classification and detection of this particular diabetic-based eye disease known as diabetic retinopathy, and also the advantage of one approach over the other is also described. Along with the approaches, the dataset and the evaluation metrics useful for DR detection and classification are also discussed. The main finding of this study is to aware researchers about the different challenges occurs while detecting diabetic retinopathy using computer vision, deep learning techniques. Therefore, a purpose of this review paper is to sum up all the major aspects while detecting DR like lesion identification, classification and segmentation, security attacks on the deep learning models, proper categorization of datasets and evaluation metrics. As deep learning models are quite expensive and more prone to security attacks thus, in future it is advisable to develop a refined, reliable and robust model which overcomes all these aspects which are commonly found while designing deep learning models.

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Diabetic Retinopathy Image Classification Using Deep Neural Network

Cited by 2 publications

References 6 publications

Optimizing Ocular Pathology Classification with CNNs and OCT Imaging: A Systematic and Performance Review

Optimizing Ocular Pathology Classification with CNNs and OCT Imaging: A Systematic and Performance Review

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

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