Open Access

ARTICLE

FASTER–RCNN for Skin Burn Analysis and Tissue Regeneration

C. Pabitha^*, B. Vanathi

Department of Computer Science and Engineering, SRM Valliammai Engineering College, Chennai, 603203, India

* Corresponding Author: C. Pabitha. Email:

Computer Systems Science and Engineering 2022, 42(3), 949-961. https://doi.org/10.32604/csse.2022.021086

Received 22 June 2021; Accepted 19 August 2021; Issue published 08 February 2022

Abstract

Skin is the largest body organ that is prone to the environment most specifically. Therefore the skin is susceptible to many damages, including burn damage. Burns can endanger life and are linked to high morbidity and mortality rates. Effective diagnosis with the help of accurate burn zone and wound depth evaluation is important for clinical efficacy. The following characteristics are associated with the skin burn wound, such as healing, infection, painand stress and keloid formation. Tissue regeneration also takes a significant amount of time for formation while considering skin healing after a burn injury. Deep neural networks can automatically assist in the extraction of features from a burn image. In our approach to burn wound analysis and regeneration of the tissue of the skin burn wound, we use the Faster RCNN (Regional Convolutional Neural Network), which is based on their severity of the burn wound. The success rates of skin cure for burning injuries can be dramatically increased with the use of different skin replacements. Our objective is to analyze different deep learning techniques that may help to analyze and classify burn wounds in a superficial, partial and complete thickness, while treating burn wounds more accurately. The application of Faster RCNN effectively classifies wound without first degree, second and third degree confusion, thus providing a suitable solution to burning wounds. The advancement in the field of profound training offers an important path in the field of the processing and burning of trauma.

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Keywords

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