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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Indonesian Journal of Electrical Engineering and Computer Science
Orimogunje, Abidemi
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Economics, Econometrics & Finance

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Development of a web-based application for real-time eye disease classification system using artificial intelligence Okokpujie, Kennedy; Tolulope, Adekoya; Orimogunje, Abidemi; Mommoh, Joshua Sokowonci; Ijeh, Adaora Princess; Ogundele, Mary Oluwafeyisayo
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i2.pp558-574

Abstract

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.
Classification model for infectious lung diseases using convolutional neural networks on web and mobile applications Okokpujie, Kennedy; Agamah, Alvin K.; Orimogunje, Abidemi; Adaora, Ijeh Princess; Omolara, Olusanya Olamide; Daramola, Samuel Adebayo; Awomoyi, Morayo Emitha
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp410-424

Abstract

Accurate lung disease diagnosis in infected patients is critical for effective treatment. Tuberculosis, COVID-19, pneumonia, and lung opacity are infectious lung diseases with visually similar chest X-ray presentations. Human expertise can be susceptible to errors due to fatigue or emotional factors. This research proposes a real-time deep learning-based classification system for lung diseases. Three models of convolutional neural networks (CNNs) were deployed to classify lung illnesses from chest X-ray images: MobileNetV3, ResNet-50, and InceptionV3. To evaluate the effect of high interclass similarity, the models were evaluated in 3-class (Tuberculosis, COVID-19, normal), 4-class (lung opacity, tuberculosis, COVID-19, normal), and 5-class (tuberculosis, lung opacity, pneumonia, COVID-19, normal) modes. The best classification accuracy was attained by retraining MobileNetV3, which obtained 94% and 93.5% for 5-class and 4-class, respectively. InceptionV3 had the lowest accuracy (90%, 89%, 93% for 5-, 4-, and 3-class), while ResNet-50 performed best for the 3-class setting. These findings suggest MobileNetV3's potential for accurate lung disease diagnosis from chest X-rays despite the interclass similarity, supporting the adoption of computer-aided detection systems for lung disease classification.
Co-Authors Adaora, Ijeh Princess Agamah, Alvin K. Awomoyi, Morayo Emitha Daramola, Samuel Adebayo Ijeh, Adaora Princess Mommoh, Joshua Sokowonci Ogundele, Mary Oluwafeyisayo Okokpujie, Kennedy Omolara, Olusanya Olamide Tolulope, Adekoya