Deep Reinforcement Learning and Capsule Networks for Advanced Bone Cancer Detection

The precise identification of the bone cancer plays a significant role in the proper diagnosis and optimized preparation of the subsequent therapy. So, enabling a fast response and management action plans corresponding to the type and stage of cancer, the early diagnosis significantly contributes to enhancing the patient’s quality of life. In this work, the approach of using as the latest innovation in the classification of the disease known as bone cancer is employed. The method comprises two more advanced techniques, namely Deep Reinforcement Learning (DRL) and Capsule Network (CapsNets) to enhance the diagnostic accuracy and efficiency. For each once, the medical pictures go through cycles of interaction with the DRL model in learning the most effective strategies for making decisions, ensuring the model’s optimal performance irrespective of the degree of data complexity. Besides, CapsNet improve feature learning and representation on the one hand, and reveal intricate spatial relations in the bone images on the other hand. Therefore, the findings illustrate that the approach possesses great performance prospects for effective and reliable clinical application, with a high accuracy rate of 98.5%. It also enhances the functioning of healthcare by giving early and precise identification of malignant bone cancers, thereby increasing bone cancer diagnosis. Major improvement has been achieved when combining DRL and CapsNet to enhance the application of AI in improving the accuracy of cancer diagnoses, translating to better treatment plans for individual patients.

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