A Deep Learning-Based Mobile Application for Monkeypox Detection

Author:

Alhasson Haifa F.1ORCID,Almozainy Elaf1,Alharbi Manar1,Almansour Naseem1,Alharbi Shuaa S.1ORCID,Khan Rehan Ullah1ORCID

Affiliation:

1. Department of Information Technology, College of Computer, Qassim University, Buraydah 52571, Saudi Arabia

Abstract

The recent outbreak of monkeypox has raised significant concerns in the field of public health, primarily because it has quickly spread to over 40 countries outside of Africa. Detecting monkeypox in its early stages can be quite challenging because its symptoms can resemble those of chickenpox and measles. However, there is hope that potential use of computer-assisted tools may be used to identify monkeypox cases rapidly and efficiently. A promising approach involves the use of technology, specifically deep learning methods, which have proven effective in automatically detecting skin lesions when sufficient training examples are available. To improve monkeypox diagnosis through mobile applications, we have employed a particular neural network called MobileNetV2, which falls under the category of Fully Connected Convolutional Neural Networks (FCCNN). It enables us to identify suspected monkeypox cases accurately compared to classical machine learning approaches. The proposed approach was evaluated using the recall, precision, F score, and accuracy. The experimental results show that our architecture achieves an accuracy of 0.99%, a Recall of 1.0%, an F-score of 0.98%, and a Precision of 0.95%. We believe that such experimental evaluation will contribute to the medical domain and many use cases.

Funder

Deanship of Scientific Research, Qassim University

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference39 articles.

1. Qureshi, M., Khan, S., Bantan, R.A., Daniyal, M., Elgarhy, M., Marzo, R.R., and Lin, Y. (2022). Modeling and Forecasting Monkeypox Cases Using Stochastic Models. J. Clin. Med., 11.

2. Irmak, M.C., Aydin, T., and Yağanoğlu, M. (November, January 31). Monkeypox Skin Lesion Detection with MobileNetV2 and VGGNet Models. Proceedings of the Medical Technologies Congress (TIPTEKNO), Antalya, Turkey.

3. Monkeypox: An emerging zoonotic disease with pandemic potential;Rahim;BIRDEM Med. J.,2022

4. Re-emergence of monkeypox in Nigeria: A cause for concern and public enlightenment;Fowotade;Afr. J. Clin. Exp. Microbiol.,2018

5. Correlation between monkeypox viral load and infectious virus in clinical specimens;Lim;J. Clin. Virol.,2023

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2. Automatic Monkeypox Disease Detection from Preprocessed Images using MobileNetV2;2024 Tenth International Conference on Bio Signals, Images, and Instrumentation (ICBSII);2024-03-20

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