The high prevalence of eye diseases these days is a critical health problem. With everyday use of digital products, the diseases are becoming increasingly common, and underlines the urgent demand for early diagnosis and timely treatment. In that context, multi-modality image fusion has recently attracted much interest for automated detection of ocular disorders like glaucoma, cataracts, diabetic retinopathy (DR), high myopia and macular degeneration. An innovative and fully automated deep learning framework, H-CapsNet, has been designed in this study for robust yet accurate eye disease classification. The suggested framework consists of a structured pipeline that involves preprocessing, characteristic extraction, attribute selection and categorization. For instance, a combined deep neural network architecture called H-Net and an Attention Block is used to initially extract a wide range of distinguishing features from the retinal images. A SENet Block further enhances the robustness of these representations so that multi-modality retinal image features can be fused into a unified description. The new Hybrid RemoPel algorithm is then applied for an optimum feature selection, whereby the most informative attributes are preserved. Both starts and ends are different. Finally, the most advanced clustering-based binary grey wolf optimizer (KCBGWO) soft capsule model through KCBGWO optimization process is utilized for final classification. The appropriate means of categorizing different kinds of eye diseases, the suggested system can help improve the accuracy of diagnosis and drive forward a new trend in ophthalmic disease detection using deep learning.
Kamal et al. (Fri,) studied this question.