Transforming Bone Cancer Diagnosis with Innovative X-Ray Techniques

Bone tumor detection is identified as one of the most important tasks in the field of medical diagnostics and it majorly depends on X-ray imaging for removing abnormalities the sooner a case is identified the more effective the treatment will be, as many options are dependent on early detection; however, these methods can be very costly and open to human error. The development will feature an automated bone tumor detecting method that utilizes deep learning methodology with X-ray images. Using Convolutional neural networks (CNN) as the classifiers and recognizers of bone tumors, the system enhances diagnosis efficiency and accuracy. It utilizes techniques for image preprocessing, such as normalization and enhancement so that it can perform better. Feature extraction is done using advanced deep learning models like CNN Models, and transfer learning finetunes pre-trained models to detect tumors efficiently even with less data set. Problems about Data imbalance, image quality heterogeneity and tumor segmentation are solved by data augmentation and large segmentation models like U-Net. Furthermore, interpretability of the model by deploying explainable AI method (e.g., Grad-CAM) builds the confidence in an automatic generated result. Aims & Objectives: To develop a reliable automated system to aid radiologists in the early-stage diagnosis and reduce time for early-stage detection improving healthcare value especially in an area where a skilled radiologist is limited.