Abubakar, A.; Ajuji, M.; Usman Yahya, I. Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination. Appl. Syst. Innov.2020, 3, 20.
Abubakar, A.; Ajuji, M.; Usman Yahya, I. Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination. Appl. Syst. Innov. 2020, 3, 20.
Abubakar, A.; Ajuji, M.; Usman Yahya, I. Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination. Appl. Syst. Innov.2020, 3, 20.
Abubakar, A.; Ajuji, M.; Usman Yahya, I. Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination. Appl. Syst. Innov. 2020, 3, 20.
Abstract
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 pre-trained deep learning models due to deficient dataset to train a new model from scratch. 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 of approximately 99.9%.
Keywords
Burns; Pressure Ulcer; Bruises; Deep Learning; Classification
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
