Detection of bone tumor from bone x-ray images using CNN classifier comparing with D-TREE classifier to improve accuracy rate

This study primarily aims to compare the performance of Convolutional Neural Networks (CNNs) and Decision Trees (D-Trees) in detecting cancer cells in x-ray images of the bone. This study makes use of data that is already accessible to the public in the NTHU Computer Vision Lab database. Both Group 1 and Group 2 had 140 photos. The computation was performed using G-power 0.8 and used a 95% confidence interval, an alpha of 0.05, and a beta of 0.2. We used 280 photos as our sample size for tumor cell identification and classification utilizing bone x-ray photographs. Convolutional Neural Networks (CNNs) and Decision Trees (D-Trees) categorize cancer cells in bone x-ray pictures with a sample size of 10. Compared to the Decision Tree (D-Tree) classifier, which had an accuracy rate of 91.0934%, the CNN classifier achieved a superior rate of 97.9034%. A p-value of 0.022 indicates that the research was statistically significant. Convolutional Neural Networks (CNNs) outperform Decision Trees (D-Trees) in effectively detecting and classifying malignancy cells from bone x-ray images.