Ocular Diseases Detection using Recent Deep Learning Techniques

Guergueb, Takfarines; Akhloufi, Moulay A.

doi:10.1109/embc46164.2021.9629763

Cited by 10 publications

(3 citation statements)

References 7 publications

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“…A model with a sensitivity score of 95.6 percent and a specificity score of 92.2 percent yields an AUC value of 0.98. Using optical coherence tomography (OCT) pictures, this work [ 35 ] was able to diagnose distinct retinal diseases. Convolutional neural networks, such as GoogLeNet, were tuned to produce this approach.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning for Ocular Disease Recognition: An Inner-Class Balance

Khan

Tafshir

Alam

et al. 2022

Computational Intelligence and Neuroscience

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It can be challenging for doctors to identify eye disorders early enough using fundus pictures. Diagnosing ocular illnesses by hand is time-consuming, error-prone, and complicated. Therefore, an automated ocular disease detection system with computer-aided tools is necessary to detect various eye disorders using fundus pictures. Such a system is now possible as a consequence of deep learning algorithms that have improved image classification capabilities. A deep-learning-based approach to targeted ocular detection is presented in this study. For this study, we used state-of-the-art image classification algorithms, such as VGG-19, to classify the ODIR dataset, which contains 5000 images of eight different classes of the fundus. These classes represent different ocular diseases. However, the dataset within these classes is highly unbalanced. To resolve this issue, the work suggested converting this multiclass classification problem into a binary classification problem and taking the same number of images for both classifications. Then, the binary classifications were trained with VGG-19. The accuracy of the VGG-19 model was 98.13% for the normal (N) versus pathological myopia (M) class; the model reached an accuracy of 94.03% for normal (N) versus cataract (C), and the model provided an accuracy of 90.94% for normal (N) versus glaucoma (G). All of the other models also improve the accuracy when the data is balanced.

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Section: Related Workmentioning

confidence: 99%

Deep Learning for Ocular Disease Recognition: An Inner-Class Balance

Khan

Tafshir

Alam

et al. 2022

Computational Intelligence and Neuroscience

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“…Another tactic has been to use multilabel classification [19]. The databases on ocular diseases [37,38] are very uneven. Due to this imbalance, it is challenging to correctly detect or categorize disease or even a typical retinograph image.…”

Section: Discussionmentioning

confidence: 99%

“…The performance of the proposed hybrid system was evaluated based on two publicly available datasets, i.e., the retinal fundus multi-disease image dataset (RFMiD) [37] and ODIR (Ocular Disease Intelligent Recognition) [38].…”

Section: Data Acquisitionmentioning

confidence: 99%

Deep-Ocular: Improved Transfer Learning Architecture Using Self-Attention and Dense Layers for Recognition of Ocular Diseases

Abbas,

Albathan,

Altameem

et al. 2023

Diagnostics

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It is difficult for clinicians or less-experienced ophthalmologists to detect early eye-related diseases. By hand, eye disease diagnosis is labor-intensive, prone to mistakes, and challenging because of the variety of ocular diseases such as glaucoma (GA), diabetic retinopathy (DR), cataract (CT), and normal eye-related diseases (NL). An automated ocular disease detection system with computer-aided diagnosis (CAD) tools is required to recognize eye-related diseases. Nowadays, deep learning (DL) algorithms enhance the classification results of retinograph images. To address these issues, we developed an intelligent detection system based on retinal fundus images. To create this system, we used ODIR and RFMiD datasets, which included various retinographics of distinct classes of the fundus, using cutting-edge image classification algorithms like ensemble-based transfer learning. In this paper, we suggest a three-step hybrid ensemble model that combines a classifier, a feature extractor, and a feature selector. The original image features are first extracted using a pre-trained AlexNet model with an enhanced structure. The improved AlexNet (iAlexNet) architecture with attention and dense layers offers enhanced feature extraction, task adaptability, interpretability, and potential accuracy benefits compared to other transfer learning architectures, making it particularly suited for tasks like retinograph classification. The extracted features are then selected using the ReliefF method, and then the most crucial elements are chosen to minimize the feature dimension. Finally, an XgBoost classifier offers classification outcomes based on the desired features. These classifications represent different ocular illnesses. We utilized data augmentation techniques to control class imbalance issues. The deep-ocular model, based mainly on the AlexNet-ReliefF-XgBoost model, achieves an accuracy of 95.13%. The results indicate the proposed ensemble model can assist dermatologists in making early decisions for the diagnosing and screening of eye-related diseases.

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Classification of Ocular Diseases: A Vision Transformer-Based Approach

Gummadi,

Ghosh

2023

Lecture Notes in Networks and Systems

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Ocular Diseases Detection using Recent Deep Learning Techniques

Cited by 10 publications

References 7 publications

Deep Learning for Ocular Disease Recognition: An Inner-Class Balance

Deep Learning for Ocular Disease Recognition: An Inner-Class Balance

Deep-Ocular: Improved Transfer Learning Architecture Using Self-Attention and Dense Layers for Recognition of Ocular Diseases

Classification of Ocular Diseases: A Vision Transformer-Based Approach

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