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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) PROCEEDING IC-ITECHS 2014 Galaksi
Shalsadilla, Shafatyra Reditha
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Enhancing ResNet with Ghost Weight Normalization For Improved Retina Disease Classification Baihaqi, Galih Restu; Shalsadilla, Shafatyra Reditha; Argaputri, Maulida Khairunisa
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1554

Abstract

Retinal disease is a dangerous disease. If left untreated, it can cause blurred vision and even cause permanent blindness. Recently, deep learning approaches are widely used to classify medical diseases. A widely used model to classify medical diseases is ResNet. To train the ResNet model, the data used is data obtained from Kaggle with the name Retinal OCT Images (Optical Coherence Tomography) consisting of 4 classes namely choroidal neovascularization (CNV), DRUSEN, diabetic macular edema (DME), and Normal with a total of 83,600 data. The ResNet base model showed accuracy and f1-score of 92%. Modifying the ResNet Base model with the addition of Ghost Weight Normalization (GWN) which aims to provide more weight normalization opportunities shows an increase in accuracy and f1-score to 94%. GWN can also increase the accuracy of CNN Base from 77% to 81%. This improvement shows that GWN can improve the accuracy of Deep learning models with its weight normalization variation technique. Although the training load and training time when using GWN can increase, the accuracy and f1-score of the ResNet model with GWN of 94% can make the chance of misclassification of retinal diseases smaller.
Accuracy Improvement of Convolutional Neural Network with Ghost Weight Normalization for Pneumonia Classification Baihaqi, Galih Restu; Shalsadilla, Shafatyra Reditha; Argaputri, Maulida Khairunisa
Jurnal Galaksi Vol. 1 No. 3 (2024): Galaksi - Desember 2024
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v1i3.35

Abstract

Pneumonia is a critical respiratory condition that requires accurate and timely diagnosis to ensure effective treatment. In this study, we propose the integration of Ghost Weight Normalization (GWN) into a Convolutional Neural Network (CNN) to enhance the accuracy and performance of pneumonia detection. The dataset used was derived from the Kaggle repository, comprising 5,856 chest X-ray images divided into two classes: Normal and Pneumonia. The CNN + GWN model demonstrated improved classification metrics with an accuracy, precision, recall, and F1-score of 95%, outperforming the CNN-Based model, which achieved 92%. While the CNN + GWN model required slightly longer training time and more epochs to achieve its best performance, the trade-off resulted in more robust and reliable predictions. The enhanced performance is attributed to the ability of GWN to normalize weights effectively, providing diverse normalization variations and improving training stability. These results underscore the potential of the CNN + GWN model for reliable pneumonia detection and highlight its capability to address the limitations of conventional CNN architectures.
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 Argaputri, Maulida Khairunisa Baihaqi, Galih Restu Lailil Muflikhah Maulidiya, Afifulail Maya Nur