Convolution Neural Network Approach for Accident Severity Detection and Hospital Selection

Abstract

Road accidents remain a critical public safety issue, necessitating rapid injury assessment and timely hospital recommendations. This project proposes a solution using Convolutional Neural Networks (CNNs) for accurate injury classification and severity detection. By leveraging deep learning, the system can analyze images of injuries to determine severity levels and suggest suitable hospitals based on the injury type. This innovative approach significantly outperforms traditional machine learning models in terms of accuracy and efficiency. Data augmentation techniques further enhance the dataset’s diversity, improving model robustness. Experimental results demonstrate that CNN-based systems offer a promising and efficient framework for road accident severity detection, potentially saving lives through faster medical interventions.