Deep Learning-Based Detection and Classification of Blunt Abdominal Trauma - Lin, Huawei; Zhang, Zeyuan; Zou, Longlong; Yang, Chunlei (International Journal of Computer Science and Information Technology, 2025)
Traumatic injuries are the leading cause of death in the first four decades of human life and represent a major global public health issue. It is estimated that over 5 million people die from traumatic injuries each year worldwide. Among these, blunt abdominal trauma (BAT) accounts for approximately 75% of all blunt injuries, making it the most common type. However, in the early stages, there may be few or no obvious signs indicating serious intra-abdominal injuries, which makes assessment particularly challenging and requires a high index of clinical suspicion. In such cases, accurate and timely diagnosis of traumatic injuries is crucial. According to extensive literature research, traditional medical image classification methods often rely on manually designed feature extraction and classifiers. These methods tend to perform poorly when dealing with complex medical images. In contrast, deep learning techniques, based on end-to-end learning, can automatically learn feature representations and classification models directly from raw image data, offering better generalization performance than traditional approaches. Furthermore, there is currently a lack of computer-aided diagnosis (CAD) systems specifically designed for the detection and classification of CT images related to blunt abdominal trauma (BAT). Based on this observation, this study proposes the development of a deep learning-based CAD system aimed at supporting the diagnosis and classification of CT images for BAT.