Machine learning methods have made huge improvements to medical imaging, particularly in the utilization of CT scans for intricate trauma cases. This paper initiates an exploratory journey, employing a hybrid quantum machine learning (HQML) approach to enhance the detection accuracy of abdominal trauma. Our study meticulously curates an extensive dataset from abdominal CT scans, navigating the inherent challenges presented by the voluminous nature and complex features of such medical data. We use quantum transfer learning techniques in a new way by combining the complex details of these scans into a computing framework that combines the powerful pattern recognition of quantum computing with the stability of classical machine learning. Our approach positions itself at the lead of medical innovation, poised to refine diagnostic precision and accelerate therapeutic protocols through an advanced analytical perspective. By looking at how well classical ResNet architectures and quantum models work, we find small differences in how well they work. This shows that quantum algorithms could decode medical images with a level of good accuracy. Our findings do not merely highlight the transformative potential of quantum computing in medical diagnostics but also pave the way for ensuing explorations in the domain of hybrid quantum-classical machine learning solutions.