Deep Learning-Based Approach for Skin Burn Detection with Multi-level Classification

In most recent years, convolutional neural network (CNN) model is the detail of craftsmanship form fruitful for photograph investigation. In this exploration, we are incorporating CNN models for classification of skin burn based on visual investigation. The aim of this paper is to develop a computerized mechanism in classifying the burn based on severity and compare the accuracies of various CNN algorithms for the same. Rapid development in deep learning enables automated learning of semantics, deep features that are easily learnt which addresses the problems of existing traditional image processing. The proposed method uses deep neural network, recurrent neural network and CNN model. The training is performed using dataset of 104 images classified into degree 1, degree 2 and degree 3 depending on the severity of the burn. Experimental analysis is also provided to compare the accuracies of different methods and identify the best model with better accuracy. The proposed computerized model can aid the medical experts in diagnosing the wound and suggest appropriate treatment depending on the severity of the skin burn. The proposed model could encourage telemedicine practise with the help of modern technology to remotely diagnose the patients especially in rural areas where there could be shortage of physicians.