Enhancing Osteosarcoma Cancer Detection with Attention Mechanisms and Capsule Networks

Osteosarcoma is a type of cancer that arises in the bone cells. The most common type of primary bone cancer, it affects long bones like the arms and legs but can affect any bone. Conservative techniques often show inadequate precision in identifying these malignant growths. The proposed study combines attention mechanisms and capsule networks to improve sarcoma cancer identification from medical imaging data. The attention mechanism focuses on the most significant features within the images, thereby developing the model's capacity to capture the complex features of sarcoma tumors. The capsule network conserves spatial hierarchies and increases the vitality of feature detection. The experiments are conducted on a comprehensive dataset of osteosarcoma images, showing that the proposed method exceeds current leading techniques concerning precision, accuracy, and recall. The proposed model significantly enhances detection performance, giving an optimistic resolution for early and precise sarcoma diagnosis.