Development of a web-based application for real-time eye disease classification system using artificial intelligence

• Published in: International journal of reconfigurable and embedded systems Vol. 14; no. 2; p. 558
• Main Authors: Okokpujie, Kennedy, Tolulope, Adekoya, Orimogunje, Abidemi, Mommoh, Joshua Sokowonci, Ijeh, Adaora Princess, Ogundele, Mary Oluwafeyisayo
• Format: Journal Article
• Language: English
• Published: 01.07.2025
• ISSN: 2089-4864; 2722-2608
• DOI: 10.11591/ijres.v14.i2.pp558-574

The incorporation of artificial intelligence (AI) into the field of medicine has created new strategies in enhancing the detection of disease, with a focus on the identification of eye diseases such as glaucoma, diabetic retinopathy, and macular degeneration associated with age, which can lead to blindness if not detected and treated early enough. Driven by the need to combat blindness, which affects approximately 39 million people globally, according to the World Health Organization (WHO). This research offers a web-based, real time approach to classifying eye diseases from fundus images due to user friendliness. Three pre-trained convolutional neural network (CNN) models are adopted, namely ResNet-50, Inception-v3, and MobileNetV3. The models were trained on a dataset of 8000 fundus images subdivided into four classes: cataract, glaucoma, diabetic retinopathy, and normal eyes. The performance of the models was evaluated in 3-way (normal eye and two diseases) and 4-way (normal eye and three diseases). ResNet-50 had higher performances, with 98% and 97% accuracy in the respective classifications, compared to InceptionV3 and MobileNetV3. Consequently, ResNet-50 was used in an online application that made real-time diagnoses. This research findings reveal the potential of CNNs in the healthcare industry, particularly in reducing over-reliance on specialists and increasing access to quality diagnostic technologies. Especially in critical areas such as this with limited healthcare resources, where the technology can create significant gaps in disease detection and control.