Comparative Analysis of SVM and CNN for Pneumonia Detection in Chest X-Ray
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.
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References
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Published
2024-03-25
How to Cite
SARASWATI, Ni Wayan Sumartini; DEWI, Dewa Ayu Putu Rasmika; PIROZMAND, Poria.
Comparative Analysis of SVM and CNN for Pneumonia Detection in Chest X-Ray.
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], v. 15, n. 1, p. 38-50, mar. 2024.
ISSN 2541-5832.
Available at: <https://ojs.unud.ac.id/index.php/lontar/article/view/110005>. Date accessed: 13 nov. 2024.
doi: https://doi.org/10.24843/LKJITI.2024.v15.i01.p04.
Section
Articles
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