Osteosarcoma Cancer Detection

Osteosarcoma detection research focuses on improving the accuracy and efficiency of diagnosing this aggressive bone cancer, primarily through the analysis of radiological and histological images. Current efforts leverage deep learning models, including transfer learning architectures (like InceptionResNetV2) and transformer-based networks, to analyze these images for tumor identification, grading, and prediction of treatment response (e.g., necrosis assessment). These advancements aim to reduce inter-observer variability in diagnosis, improve prognosis accuracy, and ultimately lead to more effective personalized treatment strategies for osteosarcoma patients.