Burnt Human Skin Segmentation and Depth Classification Using Deep Convolutional Neural Network (DCNN)

Abstract

World Health Organization (WHO) manage health-related statistics all around the world by taking the necessary measures. What could be better for health and what may be the leading causes of deaths, all these statistics are well organized by WHO. Burn Injuries are mostly viewed in middle and low-income countries due to lack of resources, the result may come in the form of deaths by serious injuries caused by burning. Due to the non-accessibility of specialists and burn surgeons, simple and basic health care units situated at tribble areas as well as in small cities are facing the problem to diagnose the burn depths accurately. The primary goals and objectives of this research task are to segment the burnt region of skin from the normal skin and to diagnose the burn depths as per the level of burn. The dataset contains the 600 images of burnt patients and has been taken in a real-time environment from the Allied Burn and Reconstructive Surgery Unit (ABRSU) Faisalabad, Pakistan. Burnt human skin segmentation was carried by the use of Otsu's method and the image feature vector was obtained by using statistical calculations such as mean and median. A classifier Deep Convolutional Neural Network based on deep learning was used to classify the burnt human skin as per the level of burn into different depths. Almost 60 percent of images have been taken to train the classifier and the rest of the 40 percent burnt skin images were used to estimate the average accuracy of the classifier. The average accuracy of the DCNN classifier was noted as 83.4 percent and these are the best results yet. By the obtained results of this research task, young physicians and practitioners may be able to diagnose the burn depths and start the proper medication.