Enhanced Fracture Detection in X-rays via Transfer Learning and Data Preprocessing

Abstract

Bone fractures represent a major public health challenge, affecting millions of people worldwide each year. Diagnosis primarily relies on the interpretation of radiographic images, a task that can be complex, prone to human error, and highly dependent on the clinician’s expertise. In this context, artificial intelligence (AI) and machine learning (ML) technologies offer promising prospects for improving the accuracy and speed of fracture detection, while also reducing the workload of healthcare professionals. Following a detailed review of different techniques, including deep learning models and their performance, we propose an innovative two-stage architecture. This architecture combines region-of-interest detection with specific classification to enhance diagnostic accuracy. The methodology is fundamentally based on data preprocessing and the use of pre-trained models to optimize performance. Experimental results demonstrate the effectiveness of this approach. This system is designed to support radiological diagnosis by automating fracture detection and facilitating its integration into medical practice.

Ähnliche Arbeiten