Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination
Centre for Visual Computing, Faculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UK
Department of Computer Science, Faculty of Science, Gombe State University, Tudun Wada 760231, Nigeria
Author to whom correspondence should be addressed.
Appl. Syst. Innov. 2020, 3(2), 20; https://doi.org/10.3390/asi3020020
Received: 11 March 2020 / Revised: 6 April 2020 / Accepted: 7 April 2020 / Published: 11 April 2020
While visual assessment is the standard technique for burn evaluation, computer-aided diagnosis is increasingly sought due to high number of incidences globally. Patients are increasingly facing challenges which are not limited to shortage of experienced clinicians, lack of accessibility to healthcare facilities and high diagnostic cost. Certain number of studies were proposed in discriminating burn and healthy skin using machine learning leaving a huge and important gap unaddressed; whether burns and related skin injuries can be effectively discriminated using machine learning techniques. Therefore, we specifically use transfer learning by leveraging pre-trained deep learning models due to deficient dataset in this paper, to discriminate two classes of skin injuries—burnt skin and injured skin. Experiments were extensively conducted using three state-of-the-art pre-trained deep learning models that includes ResNet50, ResNet101 and ResNet152 for image patterns extraction via two transfer learning strategies—fine-tuning approach where dense and classification layers were modified and trained with features extracted by base layers and in the second approach support vector machine (SVM) was used to replace top-layers of the pre-trained models, trained using off-the-shelf features from the base layers. Our proposed approach records near perfect classification accuracy in categorizing burnt skin ad injured skin of approximately 99.9%.