Image Segmentation Techniques for Bone Cancer Identification in X-ray and MRI Imagery

Integrating computer science with biology is crucial for advancing bone cancer diagnostics, particularly in distinguishing between benign and malignant tumors in X-ray and MRI images. The rapid evolution of medical imaging technology necessitates updated diagnostic methods to meet modern healthcare demands. Advanced image processing techniques, such as Watershed segmentation, are pivotal in precisely defining tumor boundaries. This research focuses on enhancing bone cancer detection by evaluating various machine learning methods and optimizing image clarity. Techniques like KAZE feature extraction, combined with XGBoost algorithms, demonstrate superior accuracy in identifying malignant regions compared to traditional classifiers such as CNN, ADABoost, and Random Forest. By integrating these advanced techniques, the ability to differentiate between benign and malignant tumors is significantly improved, leading to enhanced diagnostic accuracy and early detection rates. This approach holds promise for revolutionizing clinical practices and improving patient outcomes through more effective and automated diagnostics.