Deep Learning-based Model for Abdominal Trauma Detection

Abstract

Automated detection of abdominal trauma presents a critical challenge in modern healthcare, demanding precise and efficient solutions for timely intervention. This paper proposes a novel approach leveraging deep learning techniques for the accurate detection of abdominal trauma from medical images. By harnessing the power of Convolutional Neural Networks (CNNs) and advanced image processing algorithms, our model performs remarkably in identifying traumatic injuries within abdominal scans. We meticulously curated a diverse dataset, encompassing a wide range of trauma scenarios, to train and evaluate our deep learning architecture. Our model demonstrates robustness and reliability through rigorous experimentation and validation, outperforming traditional methods and showcasing its potential as a sophisticated diagnostic tool in clinical settings. Furthermore, this research contributes to the burgeoning field of medical image analysis, shedding light on the transformative impact of deep learning in augmenting diagnostic capabilities and improving patient outcomes. Our findings underscore the significance of harnessing cutting-edge technologies to address pressing healthcare challenges, paving the way for enhanced trauma detection and personalized patient care.