Sadikur Rahman Sadik; Md. Abdul Halim Khan; Samsuddin Ahmed; Atiqur Rahman; Sadia Enam; Md. Toukir Ahmed

An EfficientNetB3 Approach for Retinal Disease Classification with XAI and Online Interface

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Author(s)

Sadikur Rahman Sadik ¹ Md. Abdul Halim Khan ¹ Samsuddin Ahmed ¹ Atiqur Rahman ² Sadia Enam ¹ Md. Toukir Ahmed ^1,*

1. Department of Internet of Things and Robotics Engineering, University of Frontier Technology, Bangladesh, Kaliakair, Gazipur-1750, Dhaka, Bangladesh

2. Department of Internet of Things and Robotics Engineering, University of Frontier Technology, Bangladesh, Kaliakoir, Gazipur-1750, Dhaka, Bangladesh

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2026.04.03

Received: 13 Jan. 2026 / Revised: 10 Apr. 2026 / Accepted: 21 Jun. 2026 / Published: 8 Aug. 2026

Index Terms

Explainable AI, Deep Learning, Eye Disease Diagnosis, Medical Imaging, Ophthalmology

Abstract

In real-world clinical settings, the growing number of patients and the shortage of experienced ophthalmologists make early and accurate diagnosis of retinal diseases increasingly challenging. Cataracts, diabetic retinopathy, and glaucoma are some of the most common causes of lifelong blindness around the world. This is why there is a need for automated diagnostic systems that can accurately diagnose and interpret clinical data. The major goal of this work is to find out if a deep learning architecture based on EfficientNetB3 and Explainable Artificial Intelligence (XAI) can accurately classify multiple types of retinal diseases while still being clear to doctors. The proposed system categorizes retinal fundus images into four groups: cataract, diabetic retinopathy, glaucoma, and normal. The dataset consisted of a balanced and publicly accessible collection of 4,217 retinal fundus pictures, processed using standard preprocessing techniques to enhance their generalizability. We chose EfficientNetB3 as the main architecture since it is better at extracting features, and we compared it to the standard convolutional neural network baselines to show how useful it is. The suggested model was 97% accurate in classifying better than Residual Network 50 (ResNet50) is 91% and Visual Geometry Group 16 (VGG 16) is 87%. The high precision, recall, and F1-scores (0.94 – 1.00), the Cohen’s kappa of 0.95, and the low logarithmic loss of 0.10 all point to reliable predictions. The receiver operating characteristic analysis yielded an AUC of 1.00 across all illness categories. Gradient-weighted Class Activation Mapping (Grad-CAM) was utilized to address the interpretability deficit in deep learning-based medical systems and to pinpoint clinically significant retinal regions that influence model predictions. The results indicate that employing XAI alongside EfficientNetB3 enhances both diagnostic precision and interpretability, hence validating its suitability as a transparent decision-support system for the automated screening of retinal disorders.

Cite This Paper

Sadikur Rahman Sadik, Md. Abdul Halim Khan, Samsuddin Ahmed, Atiqur Rahman, Sadia Enam, Md. Toukir Ahmed, "An EfficientNetB3 Approach for Retinal Disease Classification with XAI and Online Interface", International Journal of Intelligent Systems and Applications(IJISA), Vol.18, No.4, pp.41-56, 2026. DOI:10.5815/ijisa.2026.04.03

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International Journal of Intelligent Systems and Applications (IJISA)