Enhanced skin burn assessment through transfer learning: a novel framework for human tissue analysis

Abstract

Visual inspection is the typical way for evaluating burns, due to the rising occurrence of burns globally, visual inspection may not be sufficient to detect skin burns because the severity of burns can vary and some burns may not be immediately apparent to the naked eye. Burns can have catastrophic and incapacitating effects and if they are not treated on time can cause scarring, organ failure, and even death. Burns are a prominent cause of considerable morbidity, but for a variety of reasons, traditional clinical approaches may struggle to effectively predict the severity of burn wounds at an early stage. Since computer-aided diagnosis is growing in popularity, our proposed study tackles the gap in artificial intelligence research, where machine learning has received a lot of attention but transfer learning has received less attention. In this paper, we describe a method that makes use of transfer learning to improve the performance of ML models, showcasing its usefulness in diverse applications. The transfer learning approach estimates the severity of skin burn damage using the image data of skin burns and uses the results to improve future methods. The DL technique consists of a basic CNN and seven distinct transfer learning model types. The photos are separated into those displaying first, second, and third-degree burns as well as those showing healthy skin using a fully connected feed-forward neural network. The results demonstrate that the accuracy of 93.87% for the basic CNN model which is significantly lower, with the VGG-16 model achieving the greatest accuracy at 97.43% and being followed by the DenseNet121 model at 96.66%. The proposed approach based on CNN and transfer learning techniques are tested on datasets from Kaggle 2022 and Maharashtra Institute of Technology open-school medical repository datasets that are clubbed together. The suggested CNN-based approach can assist healthcare professionals in promptly and precisely assessing burn damage, resulting in appropriate therapies and greatly minimising the detrimental effects of burn injuries.

Keywords:

• Burn injuries
• deep learning (DL)
• transfer learning
• feature extraction
• data augmentation

Acknowledgements

The investigators want to acknowledge Dr. Aashish Gambhir, MBBS MAMC New Delhi, DRM (Nuclear Medicine) Gold Medalist INMAS New Delhi, and DNB Nuclear Medicine from Apollo Hospitals Chennai with Radiation Safety Officer RSO level II and III (Nuclear Medicine Therapy) certification from AERB, Consultant and Head, Nuclear Medicine, Artificial Intelligence in Medical Imaging and Theragnostic.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.