Deep transfer learning approaches for Monkeypox disease diagnosis

Md Manjurul Ahsan; Muhammad Ramiz Uddin; Md Shahin Ali; Md Khairul Islam; Mithila Farjana; Ahmed Nazmus Sakib; Khondhaker Al Momin; Shahana Akter Luna

doi:10.1016/j.eswa.2022.119483

Deep transfer learning approaches for Monkeypox disease diagnosis

Expert Syst Appl. 2023 Apr 15:216:119483. doi: 10.1016/j.eswa.2022.119483. Epub 2023 Jan 5.

Authors

Md Manjurul Ahsan¹, Muhammad Ramiz Uddin², Md Shahin Ali³, Md Khairul Islam³, Mithila Farjana², Ahmed Nazmus Sakib⁴, Khondhaker Al Momin⁵, Shahana Akter Luna⁶

Affiliations

¹ Industrial and Systems Engineering, University of Oklahoma, Norman, OK 73019, USA.
² Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, 73019, USA.
³ Department of Biomedical Engineering, Islamic University, Kushtia 7003, Bangladesh.
⁴ Department of Aerospace and Mechanical Engineering, University of Oklahoma, Norman, OK, 73019, USA.
⁵ Department of Civil Engineering, Daffodil International University, Dhaka, 1341, Bangladesh.
⁶ Medicine & Surgery, Dhaka Medical College & Hospital, Dhaka, 1000, Bangladesh.

Abstract

Monkeypox has become a significant global challenge as the number of cases increases daily. Those infected with the disease often display various skin symptoms and can spread the infection through contamination. Recently, Machine Learning (ML) has shown potential in image-based diagnoses, such as detecting cancer, identifying tumor cells, and identifying coronavirus disease (COVID)-19 patients. Thus, ML could potentially be used to diagnose Monkeypox as well. In this study, we developed a Monkeypox diagnosis model using Generalization and Regularization-based Transfer Learning approaches (GRA-TLA) for binary and multiclass classification. We tested our proposed approach on ten different convolutional Neural Network (CNN) models in three separate studies. The preliminary computational results showed that our proposed approach, combined with Extreme Inception (Xception), was able to distinguish between individuals with and without Monkeypox with an accuracy ranging from 77% to 88% in Studies One and Two, while Residual Network (ResNet)-101 had the best performance for multiclass classification in Study Three, with an accuracy ranging from 84% to 99%. In addition, we found that our proposed approach was computationally efficient compared to existing TL approaches in terms of the number of parameters (NP) and Floating-Point Operations per Second (FLOPs) required. We also used Local Interpretable Model-Agnostic Explanations (LIME) to explain our model's predictions and feature extractions, providing a deeper understanding of the specific features that may indicate the onset of Monkeypox.

Keywords: Deep learning; Disease diagnosis; Image processing; Machine learning; Monkeypox virus.