The AI Revolution: Deep Learning’s Role in Abdominal Trauma Detection

Abdominal trauma is a significant injury that impacts the internal organs within the abdomen, including the abdominal wall, liver, spleen, pancreas, kidneys, stomach, colon, and bladder, among others. The nature and severity of these injuries can vary widely based on the specific circumstances and forces involved, making it challenging to make broad predictions about mortality rates and the necessity for surgical intervention. Globally, abdominal trauma ranks as a leading cause of death, and the use of CT scans has become crucial in assessing patients with suspected abdominal injuries due to their ability to provide detailed cross-sectional images. Recently, deep learning techniques have emerged as valuable tools to assist healthcare professionals in rapidly and accurately identifying injuries and determining their severity. In this research, Convolutional Neural Networks (CNNs) are employed to detect abdominal trauma injuries affecting different organs, such as the bowel, extravasation, liver, kidney, and spleen. Various deep learning networks namely, CNN-ResNet-50, CNN-MobileNetV3, and CNN-ImageNet are utilized in this approach, and the model’s efficiency is assessed using a range of evaluation measures. Empirical findings demonstrate that the CNN-ImageNet model excels in detecting abdominal trauma injuries when compared to existing algorithms.