Journal of evaluation in clinical practice · 2025
Optimised Hybrid Attention-Based Capsule Network Integrated Three-Pathway Network for Chronic Disease Detection in Retinal Images.
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Mohamed Yaseen M, Vijayan Thulasi Bai
Аннотация
BACKGROUND: Over the past 20 years, researchers have concentrated on generating retinal images as a means of detecting and classifying chronic diseases. Early diagnosis and treatment are essential to avoid chronic diseases. Manually grading retinal images is time-consuming, prone to errors, and lacks patient-friendliness. Various Deep Learning (DL) algorithms are employed to detect chronic diseases from retinal fundus images. Also, these methods have some disadvantages, such as overfitting, computational cost, and so on.
OBJECTIVE: The proposed research aims to develop Optimized DL based system for detecting chronic diseases in retinal images and solving existing issues.
METHODOLOGY: Initially, the retinal images are pre-processed to clean and organize the data. Normalization and HSI Colour Conversion are the techniques used for pre-processing. Inception-V3, ResNet-152 and a Convolutional Vision Transformer (Conv-ViT) are used to perform feature extraction. The classifier is an Optimized Hybrid Attention-based Capsule Network. An optimization is included in the proposed model to increase the classifier s performance.
RESULTS: The proposed approach attains accuracies of 99.05 % and 99.15% using Diabetic Retinopathy 224 × 224 (2019 Data) and the APTOS-2019 dataset, respectively. The superior performance of the proposed technique highlights its effectiveness in this domain.
CONCLUSION: The implementation of such automated methods can significantly improve the efficiency and accuracy of chronic disease diagnosis, benefiting both healthcare providers and patients.
OBJECTIVE: The proposed research aims to develop Optimized DL based system for detecting chronic diseases in retinal images and solving existing issues.
METHODOLOGY: Initially, the retinal images are pre-processed to clean and organize the data. Normalization and HSI Colour Conversion are the techniques used for pre-processing. Inception-V3, ResNet-152 and a Convolutional Vision Transformer (Conv-ViT) are used to perform feature extraction. The classifier is an Optimized Hybrid Attention-based Capsule Network. An optimization is included in the proposed model to increase the classifier s performance.
RESULTS: The proposed approach attains accuracies of 99.05 % and 99.15% using Diabetic Retinopathy 224 × 224 (2019 Data) and the APTOS-2019 dataset, respectively. The superior performance of the proposed technique highlights its effectiveness in this domain.
CONCLUSION: The implementation of such automated methods can significantly improve the efficiency and accuracy of chronic disease diagnosis, benefiting both healthcare providers and patients.