Classification of Fractured Bones Using Machine Learning

Abstract

This study uses advanced machine learning approaches to create an effective system for classifying damaged bones. The primary method utilized is supervised learning, specifically the Random Forest algorithm. This algorithm is applied to detect and categorize bone fractures using the Musculoskeletal Radiographs (MURA) dataset, which includes various X-ray images of different human bone categories. The proposed method involves several key steps: gathering and uploading datasets, extracting relevant features, dividing the data into training and testing sets, creating and training the Random Forest model, and finally, uploading test images for bone classification. The project's primary focus is to enhance the accuracy of bone fracture identification. The project has specific hardware and software requirements and is implemented using Python and various supporting libraries. This approach aims to significantly improve the efficiency and reliability of diagnosing bone fractures, ultimately contributing to better patient outcomes in medical practice.