Comparative Analysis of SVM and CNN for Pneumonia Detection in Chest X-Ray

Abstract

Recognizing pneumonia can be done by analyzing chest X-rays. Pneumonia sufferers experience pleural effusion, fluid between the lungs’ layers. It causes the lungs’ X-ray picture to be cloudy. It differs from the X-rays on normal lungs, which are dark. This difference is the characteristic of the data so that it can be classified. Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) were employed in this study to identify pneumonia in X-ray images. SVM optimizes the hyperplane to separate data classes, while CNN uses convolution and pooling layers to learn patterns in the image. The data are obtained from General Hospital Ganesha Gianyar Bali and research by J.P. Cohen et al. CNN has several capabilities, such as automatic feature extraction, divided parameters, position invariance, and good generalization, so that it can classify limited data. This research applied Principal Component Analysis (PCA) and Wavelet Transformation to support both methods.  The PCA-SVM model gave the best performance. 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.