Detection of Diabetic Retinopathy Using Hybrid InceptionResNetV2-KELM Method
DOI:
https://doi.org/10.30871/jaic.v10i1.11967Keywords:
Convolutional Neural Network (CNN), Diabetes Mellitus, Diabetic Retinopathy, InceptionResNetV2, Kernel Extrem Learning Machine (KELM)Abstract
Diabetic Retinopathy (DR) is a complication of Diabetes Mellitus (DM), both type 1 and type 2 DM. Based on its severity, DR is divided into mild DR, moderate DR, severe DR, and proliferative DR stages. Manual detection is difficult because there is a fairly small difference between normal and DR. The Computer-Aided Diagnosis (CAD) system is a solution for detecting the severity of DR quickly and accurately so that DR sufferers do not get worse, which can cause blindness. This study uses fundus images from the Mesindor dataset consisting of four classes, namely normal, mild DR, moderate DR, and severe DR, with the InceptionResNetV2-KELM hybrid method. InceptionResNetV2 is used as a feature extraction and Kernel Extreme Learning Machine (KELM) as its classification. Several types of kernels are applied as model trials. The results show the highest sensitivity lies in the polynomial kernel experiment with a sensitivity value of 99.88%, an accuracy of 99.88%, and a specificity of 99.96%. The method used is able to detect very well and is quite time-effective compared to conventional CNN.
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Copyright (c) 2026 Musfiroh Musfiroh, Dian C Rini Novitasari, Lutfi Hakim, Adelia Damayanti, Dina Zatusiva Haq, Siti Nur Aisah
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