CNN and SVM Based Classifier Comparation to Detect Lung Nodule In Computed Tomography Images

Abstract

Convolutional Neural Networks (CNN) are natural based classification algorithm that combine Multiple Layer Perceptron (MLPs). Meanwhile, support vector machines (SVM) is a mathematical-based classification algorithm that naturally have supervised learning models. In some research related to image processing, each algorithm has its owned supremacy as well as the drawback. None of the previous studies compare both algorithm when they are utilized to detect nodule located in the pulmonary or lung images produced by Computed Tomography (CT) scan. Hence, this research comparing the two algorithms in case of lung nodule detection in CT images, since detecting lung nodule in CT images is still challenging. SVM-based classifier is preceded by feature extraction as its common behavior of mathematical based classifier. There are three algorithms use to conduct feature extraction process, namely Hu moment invariant, Haralick and Color Histogram extraction. In the opposite, CNN-based classifier consists of three layers convolution for training and testing steps. The result shows that SVM has better results than CNN in case of computing speed. Meanwhile have a better accuracy in detecting lung nodule. The results of the test analysis show that the extractor feature when preprocessing conduct before being classified by SVM makes the computing process faster. The accuracy of SVM-based classifier can be improved by adjusting some computation variables in feature extraction stages, such as adding more bins in the color histogram extraction. Those adjustment will lead to more computation times.

Texture Analysis of Breast DCE-MRI to Predict Benign and Malignant Tumors and HER-2 Status Using Machine Learning and Deep Learning

“…Zhou et al [28] found that deep learning achieved a higher accuracy rate compared to ROI-based radiology when evaluating the accuracy of mass lesions detected on breast DCE-MRI. Sentana et al [38] indicated that CNN had a higher accuracy than SVM on the detection of lung nodules. Nyflot et al [37] have used CNN and four machine learning classifiers to evaluate quality of images from patients who underwent intensity-modulated radiotherapy.…”