Abstract
This paper explores the use of a convolutional neural network (CNN) in burn skin diagnosis and prognosis. Leveraging a variety of labelled medical images, the model integrates to acquire comprehensive features. By enhancing diagnostic and prognostic accuracy, the model aims to boost the outcomes of dermatological care. When compared to conventional techniques, the CNN performs better for provisional diagnosis, obtaining high accuracy in classifying burn severity. By estimating possible outcomes based on the original evaluation, the model is further expanded to offer a prediction of the healing process. In relation to treatment plans and long-term patient care, this expertise allows plastic surgeons to make informed decisions. Considering consideration of different clinical settings and patient demographics, we assess the suggested method on an extensive dataset of burn skin photos. The outcomes demonstrate that the CNN can diagnose and predict burn skin damage. Our results imply that using advanced deep learning methods in the plastic surgery workflow can greatly improve the accuracy and effectiveness of burn-related analyses.