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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)
Maulidiya, Afifulail Maya Nur
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Computer Science & IT Electrical & Electronics Engineering

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Journal : Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)

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Enhancing DenseNet Accuracy in Retinal Disease Classification with Contrast Limited Adaptive Histogram Equalization Baihaqi, Galih Restu; Shalsadilla, Shafatyra Reditha; Maulidiya, Afifulail Maya Nur; Muflikhah, Lailil
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.30327

Abstract

Retinal diseases are serious conditions that can cause vision impairment and, in severe cases, blindness, affecting 6.3% to 17.9% of cases per 100,000 people annually worldwide. Early diagnosis is crucial but often time-consuming, prompting the use of Artificial Intelligence (AI) models like DenseNet, part of the Convolutional Neural Network (CNN) architecture, to streamline the process. This study utilizes the Retinal OCT Images dataset from Kaggle, comprising 83,600 images categorized into four classes. To address the low contrast in Optical Coherence Tomography (OCT) images, the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique was applied during preprocessing. Results indicate that DenseNet without CLAHE achieved an accuracy, precision, recall, and F1-score of 95%, while incorporating CLAHE improved these metrics to 98%. The application of CLAHE also reduced classification bias and error, enhancing model reliability despite requiring more training epochs (43 compared to 39 without CLAHE). These findings demonstrate the potential of CLAHE to optimize DenseNet performance in retinal disease classification. Future research could explore other image enhancement techniques or apply the method to different retinal disease datasets, contributing to improved diagnostic accuracy in clinical settings.
Co-Authors Baihaqi, Galih Restu Lailil Muflikhah Shalsadilla, Shafatyra Reditha