Article: A novel architecture for diabetic and glaucoma detection using a multi-layer convolutional neural network system Journal: International Journal of System of Systems Engineering (IJSSE) 2025 Vol.15 No.3 pp.197 - 214 Abstract: The proposed effort aims to develop a more predictive model for identifying diabetic retinopathy (DR) and Glaucoma, two major retinal illnesses that cause blindness in working-age individuals globally. Colour retinal photographs are challenging and time-consuming to diagnose DR and Glaucoma. A multilayer CNN model detects DR and Glaucoma. CNN's enhancements improve diagnosis accuracy. The model can better capture retinal picture subtleties by adding data, improving diagnostic performance. 3658 retinal pictures in five categories are used for the DR problem from the Kaggle dataset. With 1103 retinal pictures and two class labels, RIGA is used for Glaucoma. Using these datasets, the proposed technique easily identifies healthy and diseased retinal pictures, reducing physician evaluations. Using two publicly available datasets, the suggested model had a prediction accuracy of 98%. These results show the model can identify retinal images and detect DR and Glaucoma. Finally, data-augmented multilayer CNN models improve DR and glaucoma diagnosis. Accuracy comes from detail and large datasets. Data augmentation and multilayer CNN models improved it. The model may detect and treat severe retinal problems earlier. Inderscience Publishers - linking academia, business and industry through research

Title: A novel architecture for diabetic and glaucoma detection using a multi-layer convolutional neural network system

Authors: Neha Sewal; Charu Virmani

Addresses: Department of Computer Science, Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, 121004, India ' Department of Computer Science, Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, 121004, India

Abstract: The proposed effort aims to develop a more predictive model for identifying diabetic retinopathy (DR) and Glaucoma, two major retinal illnesses that cause blindness in working-age individuals globally. Colour retinal photographs are challenging and time-consuming to diagnose DR and Glaucoma. A multilayer CNN model detects DR and Glaucoma. CNN's enhancements improve diagnosis accuracy. The model can better capture retinal picture subtleties by adding data, improving diagnostic performance. 3658 retinal pictures in five categories are used for the DR problem from the Kaggle dataset. With 1103 retinal pictures and two class labels, RIGA is used for Glaucoma. Using these datasets, the proposed technique easily identifies healthy and diseased retinal pictures, reducing physician evaluations. Using two publicly available datasets, the suggested model had a prediction accuracy of 98%. These results show the model can identify retinal images and detect DR and Glaucoma. Finally, data-augmented multilayer CNN models improve DR and glaucoma diagnosis. Accuracy comes from detail and large datasets. Data augmentation and multilayer CNN models improved it. The model may detect and treat severe retinal problems earlier.

Keywords: diabetic retinopathy; Kaggle; deep learning; CNN; convolutional neural network; glaucoma; RIGA dataset; multi-layer CNN; augmentation techniques.

DOI: 10.1504/IJSSE.2025.147017

International Journal of System of Systems Engineering, 2025 Vol.15 No.3, pp.197 - 214

Received: 07 Jun 2023
Accepted: 30 Jun 2023
Published online: 10 Jul 2025 *

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Title: A novel architecture for diabetic and glaucoma detection using a multi-layer convolutional neural network system

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