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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Dewa Ayu Putu Rasmika Dewi
Department of Infectious Diseases, School of Medicine, International University of Health and Welfare, Japan
Computer Science & IT

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Comparative Analysis of SVM and CNN for Pneumonia Detection in Chest X-Ray Ni Wayan Sumartini Saraswati; Dewa Ayu Putu Rasmika Dewi; Poria Pirozmand
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 15 No 1 (2024): Vol. 15, No. 1 April 2024
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i01.p04

Abstract

Recognizing pneumonia sufferers can be done by analyzing chest X-ray images. Pneumonia sufferers experience pleural effusion, where fluid is between the lungs’ layers. It causes the lungs’ X-ray picture to be cloudy or hazy. It differs from the appearance of X-rays on normal lungs which are dark in color. These differences in X-Ray images can be classified automatically with the help of Artificial Intelligence This research used convolutional neural networks and support vector machine methods to recognize X-ray images of pneumonia. This research applied Principal Component Analysis and Wavelet Transformation support to both methods. This research aimed to evaluate the performance of each model combination. The PCA-SVM model gave the best performance, with an accuracy of 94.545% and an F1 score of 94.675%. The SVM model outperforms the CNN model in recognizing images; in this case, it could be due to the relatively small amount of training data.
Co-Authors Poria Pirozmand Saraswati, Ni Wayan Sumartini