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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal INFOTEL
Suedarmin, Muhammad
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Combination of Image Improvement on Segmentation Using a Convolutional Neural Network in Efforts to Detect Liver Disease Umilizah, Nia; Octavia, Pipin; Kesuma, Lucky Indra; Rayani, Ira; Suedarmin, Muhammad
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 2 (2024): Vol. 7 No. 2 (2024): Issues January 2024
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i2.10221

Abstract

Liver disease is a disease caused by various factors such as the spread of viruses. Liver damage causes the ability to break down red blood cells to be disrupted. Detection of liver disease can be done using the segmentation. Segmentation is useful for separating an area of the liver in an image from other areas. Segmentation carried out manually requires experts and a long time, so automatic segmentation is needed. CNN can be used to perform automatic segmentation. One of the CNN architectures is the U-Net architecture. Segmentation requires quality images to improve recognition of image patterns, so image improvement is needed in the form of contrast enhancement. Contrast improvement was carried out by taking Green Channel images. Contrast enhancement was carried out using the Contrast Stretching and CLAHE methods. The image improvement results show MSE and SSIM values 66.1844 and 0.7088. Evaluation of the image improvements obtained provides significant changes. The improved image is used at the segmentation stage. Segmentation is carried out using the U-Net architecture. The segmentation results obtained performance evaluation values in the form of accuracy 99.6%, sensitivity 98.9%, and specificity 99.7%. This shows that the proposed method can detect liver disease in liver images well
Pembelajaran Ensemble Voting Tertimbang dari Arsitektur CNN untuk Klasifikasi Retinopati Diabetik Desiani, Anita; Primartha, Rifkie; Hanum, Herlina; Dewi, Siti Rusdiana Puspa; Suprihatin, Bambang; Al-Filambany, Muhammad Gibran; Suedarmin, Muhammad
JURNAL INFOTEL Vol 16 No 1 (2024): February 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i1.999

Abstract

Diabetic Retinopathy (DR) is a diabetes disease that attacks the retina of the eye and can be recognized through retinal images. The process of assisting retinal images can be done by applying deep learning-based methods, one of which is the Convolutional Neural Network (CNN). CNN has many architectures that can perform image classification processes, namely ResNet-50, MobileNet, and EfficientNet. Weaknesses of each architecture can be overcome through ensemble learning methods that can add up the performance results of each classification method. The study applies the ensemble learning method to improve the performance of the ResNet-50, MobileNet, and EfficientNet architectures in paying for DR disease on the retina by weighted voting. The data used are the APTOS and EyePACS datasets. The method in this research is data collection, training, testing, and evaluation of each architecture and ensemble learning. The results of the superior ensemble learning performance in the value of accuracy, F1-Score, and Cohens Kappa were obtained respectively 93.3%, 93.42%, and 0.866. The best specificity value was obtained by Resnet-50 at 99.78% and the highest sensitivity value was obtained by EfficientNet at 96.2%. Based on the classification results of each architectural and ensemble learning, it can be interpreted that the proposed ensemble learning method is excellent to perform image classification for Diabetic Retinopathy.
Co-Authors Al-Filambany, Muhammad Gibran Anita Desiani Bambang Suprihatin Herlina Hanum, Herlina Kesuma, Lucky Indra Octavia, Pipin Rayani, Ira Rifkie Primartha Siti Rusdiana Puspa Dewi Umilizah, Nia