Efficient Classification of Abdomen Trauma Disease Using Neural Network Technique with Efficientnet Model

The study seeks to adopt the different machine learning and deep learning algorithms in its own right, with an initial emphasis on the EfficientNet to improve image classification for detecting organ abnormalities in healthcare. The system automates this process of detection and classifies medical images into 13 classes, including "bowel healthy," "kidney high," and "spleen injury," thereby aiding the clinician in his duties at the same time, helping to reduce errors. While existing systems based on CNNs have shown to be effective, they usually require significant computational resources with large amounts of training data. Something very different and worthwhile is achieved with EfficientNet when it gives comparable or even superior accuracy and fewer parameters, thus establishing an excellent balance between performance and computational efficiency. The whole thing is operational with just a click of an image that can be uploaded from a user-friendly Flask interface. The project shall offer the most accurate step towards automation in the diagnosis of organ-related disorders to achieve better values in patient care and hasten clinical decisions. Future studies will center on broadening the model to include other pathology conditions while making it more interpretable for medical professionals. Based on Python, the project emphasizes accessibility and ease in implementing real-life healthcare scenarios.