Detection and classification of tumor cells from bone x-ray imagery using SVM classifier with Naïve Bayes classifier

Abstract

The primary objective of this research article is to employ detection and classification of tumour cells from bone x-ray imagery by utilising the Support Vector Machine (SVM) classifier in comparison with the Naive Bayes (NB) classifier. This comparison will be made between the two classification methods. Components and Techniques: The dataset that is used in this paper makes use of the database that is housed in the computer vision lab at National Tsing Hua University (NTHU), which is open to the public. The detection and classification of tumour cells using bone x-ray images required a sample size of 280 (Group 1 = 140 and Group 2 =140), and the calculation was carried out using G-power 0.8, with alpha and beta qualities of 0.05 and 0.2, and a confidence interval of 95%. The sample size was determined by the number of tumour cells in each of the two groups. The Support Vector Machine (SVM) classifier and the Naive Bayes (NB) classifier were used to perform the detection and classification of tumour cells extracted from bone x-ray images. The number of samples used for each classification method was ten. The results show that the accuracy rate of the Support Vector Machine (SVM) classifier is 95.9034 times higher than the accuracy rate of the Naive Bayes (NB) classifier, which is 92.0934 times higher. The significance level of the study is determined to be p = 0.021. When it comes to the detection and classification of tumour cells using bone x-ray images, the Support Vector Machine (SVM) classifier yields superior results in terms of its accuracy rate when compared to the Naive Bayes (NB) classifier.