International Research Journal of Education and Technology

Bone cancer detection is a critical area in medical imaging that benefits significantly from advancements in deep learning. This research article provides a comparative analysis of various feature extraction methods employed in deep learning models for detecting bone cancer. We explore methods ranging from traditional Convolutional Neural Networks (CNNs) to advanced techniques like Generative Adversarial Networks (GANs) and attention mechanisms. Our study aims to highlight the strengths and weaknesses of each method, offering insights into their performance and applicability in clinical settings.

KEYWORDS: Bone Cancer, Deep Learning, Convolutional Neural Networks, Generative Adversarial Networks