Deep learning-based CNN for multiclassification of ocular diseases using transfer learning

Deepak, G Divya; Bhat, Subraya Krishna

doi:10.1080/21681163.2024.2335959

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2024

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Cited by 3 publications

References 48 publications

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Improved CNN architecture for automated classification of skin diseases

Deepak,

Bhat,

Gupta

2024

Computer Methods in Biomechanics and Biomedical Engineering: Im

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Improved CNN architecture for automated classification of skin diseases

Deepak,

Bhat,

Gupta

2024

Computer Methods in Biomechanics and Biomedical Engineering: Im

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Retinal Image Analysis for Disease Classification using Convolutional Neural Networks

Verma

2024

2024 8th International Conference on I-Smac (IoT in Social, Mobile, Analytics and Cloud) (I-Smac)

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A Review on Multiple-Ocular Disease Detection Methodology using ML and DL Techniques

Dr. Sheshang Degadwala,

Shivangi Samajpati

2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

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The rapid advancement in machine learning (ML) and deep learning (DL) techniques has significantly impacted the detection and diagnosis of ocular diseases, which are critical for preserving vision and overall eye health. This review aims to explore the various ML and DL methodologies applied to the detection of multiple ocular diseases, highlighting their effectiveness, limitations, and areas for improvement. The motivation behind this review stems from the increasing prevalence of ocular diseases and the need for efficient, accurate diagnostic tools. Despite the promising results of existing techniques, limitations such as data variability, the need for extensive training data, and computational resource requirements persist. The objective is to synthesize current methodologies and propose enhancements, particularly through the integration of attention mechanisms in convolutional neural networks (CNNs). This review identifies gaps in current research and suggests directions for future work to enhance diagnostic accuracy and clinical applicability.

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Deep learning-based CNN for multiclassification of ocular diseases using transfer learning

Cited by 3 publications

References 48 publications

Improved CNN architecture for automated classification of skin diseases

Improved CNN architecture for automated classification of skin diseases

Retinal Image Analysis for Disease Classification using Convolutional Neural Networks

A Review on Multiple-Ocular Disease Detection Methodology using ML and DL Techniques

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