Detection of bone tumor from bone x-ray images using KNN classifier comparing with random forest classifier to improve accuracy rate

Finding a way to make KNN Classifier, instead of Random Forest Classifier, better at identifying bone tumors in x-ray pictures is the main goal of this study. The data used in this paper’s dataset is obtained from the NTHU Computer Vision Lab, which is open to the public. We used a 95% confidence range with alpha and beta values of 0.05 and 0.2, respectively. An analysis of bone x-ray images was conducted using G-power 0.8 to ascertain the likelihood of tumor cell identification and categorization. We used a total of 180 participants, 90 from Group 1 & 90 from Group 2. A combination of K-Nearest Neighbor (KNN) & Random Forest (RF) is employed, along with a sample size of 10 individuals, to identify and classify cancer cells in bone x-ray images. According to the results, the Novel K-Nearest Neighbor (KNN) classifier outperforms the Random Forest (RF) classifier with an accuracy rating of 95.2905. The study’s results are statistically significant (p=0.027). In conclusion, when it comes to classifying tumor cells from bone x-ray pictures, K-Nearest Neighbor (KNN) outperforms Random Forest (RF) in terms of accuracy.