Automated Bone Tumor Detection Using Deep Learning: A CNN-Based Approach For Enhanced Diagnostic Accuracy

Abstract - Primary bone tumors present significant diagnostic challenges on radiographs, often requiring specialized expertise for accurate and timely identification.1 Early detection is crucial for a favorable prognosis, particularly for malignant types, which represent a leading cause of cancer-related mortality in adolescents and young adults.3 This study develops and evaluates a deep learning (DL) model, specifically Faster R-CNN with a ResNet backbone, for the automated detection and classification (benign vs. malignant) of primary bone tumors on radiographs. The model was trained and validated using the publicly available Bone Tumor X-ray Radiograph (BTXRD) dataset. The DL model demonstrates significant potential as an assistive tool for radiologists in detecting and classifying primary bone tumors on radiographs, potentially improving diagnostic accuracy and efficiency, particularly in non-specialized settings. Key Words: Bone Tumor, Deep Learning, Radiography, X-ray, Object Detection, Classification, Convolutional Neural Network (CNN), Faster R-CNN, BTXRD, Computer-Aided Diagnosis.