Feature Extraction Based Machine Learning Approach for Bone Cancer Detection

Abstract

Osteosarcoma is a type of cancer that develops in the bones. Though it can happen in any bone, it commonly happens in long bones like the legs and arms. As a result, early detection and categorization of bone cancers have become critical for treating patients. A wavelet-based segmentation algorithm was utilized in this work to detect bone cancers. The segmented bone cancer components were then processed further for categorization. The enhanced convolutional neural network (ECNN) classification was employed in this investigation to differentiate between benign and malignant bone cancers. Collect multiple photos and use wavelet transform features to extract a trained classifier model. Sensitivity (97%), Specificity (97%), Precision (98%), Accuracy (97.5%), and F1Score (97.5) are the performance metrics for the ECNN deep learning (DL) algorithm. According to the results, ECNN deep learning beats deep learning methods, including SVM, ANN, and RNN. As a result, the ECNN deep learning technology can be used to diagnose bone cancer more accurately. Based on histology pictures, our enhanced model performs at the cutting edge of detecting osteosarcoma cancer.