Modified EfficientNetB3 Deep Learning Model to Classify Colour Fundus Images of Eye Diseases

Sharma, Riya; Gangrade, Jayesh; Gangrade, Shweta; Mishra, Ashish; Kumar, Gautam; Kumar Gunjan, Vinit

doi:10.1109/icccmla58983.2023.10346769

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2024

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Cited by 3 publications

References 17 publications

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Automated Screening for Ocular Abnormalities: Leveraging Data Augmentation for Improved Diagnostic Accuracy

Nguyen,

Van Tran,

2024

Communications in Computer and Information Science

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Automated Screening for Ocular Abnormalities: Leveraging Data Augmentation for Improved Diagnostic Accuracy

Nguyen,

Van Tran,

2024

Communications in Computer and Information Science

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A Lightweight OCT Image Classification Model with Low Configuration and High Efficiency

Cao,

Lian,

Chen

et al. 2024

Lecture Notes in Computer Science

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An Efficient System for Identification of Eye Disease in Fundus Images using a Deep Transfer Learning-based Pre-trained Model

Sharma,

Wasim,

Sharma

2024

Eng. Technol. Appl. Sci. Res.

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Ophthalmologists rely heavily on retinal fundus imaging to diagnose retinal diseases. Early detection can enhance the likelihood of a cure and also prevent blindness. Retinal fundus images can be used by medical professionals to diagnose retinal conditions such as diabetic retinopathy and retinitis pigmentosa. This study proposes an automated diagnostic approach using a Deep Learning (DL) model to identify fundus images with a high prediction rate. This study aims to use multilabel classification to identify diseases in fundus images. An EfficientNet-B5-based model was trained on a fundus image dataset to classify images as normal, NPDR, and PDR. Image preprocessing was used, including conversion to RGB format, resizing to 224×224, and image filtering using the Gaussian blur algorithm. Additionally, 10-fold cross-validation was used to train and validate the proposed approach. The enhanced EfficientNet-B5 model demonstrated superior validation and training accuracy for eye disease classification compared to existing techniques, achieving 96.04% and 99.54%, respectively. This technology enables early detection and treatment of eye conditions, potentially improving patient outcomes.

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Modified EfficientNetB3 Deep Learning Model to Classify Colour Fundus Images of Eye Diseases

Cited by 3 publications

References 17 publications

Automated Screening for Ocular Abnormalities: Leveraging Data Augmentation for Improved Diagnostic Accuracy

Automated Screening for Ocular Abnormalities: Leveraging Data Augmentation for Improved Diagnostic Accuracy

A Lightweight OCT Image Classification Model with Low Configuration and High Efficiency

An Efficient System for Identification of Eye Disease in Fundus Images using a Deep Transfer Learning-based Pre-trained Model

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