Explainable Transfer Learning‐Based Deep Learning Model for Pelvis Fracture Detection
Mohamed A. Kassem, Soaad M. Naguib, Hanaa M. Hamza, Mostafa M. Fouda, Mohamed Saleh, Khalid M. Hosny
Abstract
Pelvis fracture detection is vital for diagnosing patients and making treatment decisions for traumatic pelvis injuries. Computer‐aided diagnostic approaches have recently become popular for assisting doctors in disease diagnosis, making their conclusions more trustworthy and error‐free. Inspecting X‐ray images with fractures needs a lot of time from experienced physicians. However, there is a lack of inexperienced radiologists in many hospitals to deal with these images. Therefore, this study presents an accurate computer‐aided‐diagnosing system based on deep learning for detecting pelvis fractures. In this research, we construct an explainable artificial intelligence (XAI) framework for pelvis fracture classification. We used a dataset containing 876 X‐ray images (472 pelvis fractures and 404 normal images) to train the model. The obtained results are 98.5%, 98.5%, 98.5%, and 98.5% for accuracy, sensitivity, specificity, and precision.