Monkeypox Lesion and Rash Stage Classification for Self-screening on Mobile Application Using Deep Learning Technique
Article Sidebar
Main Article Content
Abstract
The early diagnosis of pox symptoms is an important part of preventing a global pandemic. In addition, the estimation of the disease stage and time period of pox rash is of interest. Computer Aided Diagnosis systems have been developed for identification of suspected cases based on machine learning techniques. In recent years, many deep learning approaches have been developed to classify monkeypox disease. In this study, a monkeypox lesion and rash stage classification for self-screening on mobile applications using deep learning techniques was introduced. The datasets consisted of skin lesion and pox rash images. Data augmentation methods were used to increase the sample size and split data into training and testing sets in the experiment setup. When comparing overall accuracy, EfficientNet achieved an accuracy of 0.95 for pox and 0.97 for the pox rash stage. EfficientNet was selected for conversion and implementation on the mobile application. The study determined that the use of deep learning techniques for monkeypox lesions and rash stage classification on a mobile application enabled early identification of patients and effective control of community spread.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright Transfer Statement
The copyright of this article is transferred to Current Applied Science and Technology journal with effect if and when the article is accepted for publication. The copyright transfer covers the exclusive right to reproduce and distribute the article, including reprints, translations, photographic reproductions, electronic form (offline, online) or any other reproductions of similar nature.
The author warrants that this contribution is original and that he/she has full power to make this grant. The author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors.
Here is the link for download: Copyright transfer form.pdf
References
McCollum, A.M. and Damon, I.K., 2013. Human monkeypox. Clinical Infectious Diseases, 58(2), 260-267, https://doi.org/10.1093/cid/cit703.
Farahat, R.A., Ali, I., AL-Ahdal, T., Benmelouka, A.Y., Albakri, K., El-Sakka, A.A., Abdelaal, A., Abdelazeem, B., Anwar, M.M., Mehta, R., Sah, R., Rouniyar, R. and Sah, R., 2022. Monkeypox and human transmission: Are we on the verge of another pandemic? Travel Medicine and Infectious Disease, 49, https://doi.org/10.1016/j.tmaid.2022.102387.
Adler, H., Gould, S., Hine, P., Snell, L.B., Wong, W., Houlihan, C.F., Osborne, J.C, Rampling, T., Beadsworth, M.BJ., Duncan, C.J., Dunning, J., Fletcher, T.M., Hunter, E.R., Jacobs, M., Khoo, S.H., Newsholme, W., Porter, D., Porter, R.J., Ratcliffe, L., Schmid, M.L., Semple, M.G., Tunbridge, A.J., Wingfield, T. and Price, M.N., 2022. Clinical features and management of human Monkeypox: A retrospective observational study in the UK. The Lancet Infectious Diseases, 22(8), 1153-1162, https://doi.org/10.1016/s1473-3099(22)00228-6.
Li, Y., Olson, V.A., Laue, T., Laker, M.T. and Damon, I.K., 2006. Detection of Monkeypox virus with real-time PCR assays. Journal of Clinical Virology, 36(3), 194-203, https://doi.org/10.1016/j.jcv.2006.03.012.
Saijo, M., Ami, Y., Suzaki, Y., Nagata, N., Iwata, N., Hasegawa, H., Ogata, M., Fukushi, S., Mizutani, T., Iizuka, I., Sakai, K., Sata, T., Kurata, T., Kurane, I. and Morikawa, S., 2008. Diagnosis and assessment of monkeypox virus (MPXV) infection by quantitative PCR assay: differentiation of Congo Basin and West African MPXV strains. Japanese Journal of Infectious Diseases, 61(2), 140-142.
Antinori, A., Mazzotta, V., Vita, S., Carletti, F., Tacconi, D., Lapini, L.E., D’Abramo, A., Cicalini, S., Lapa, D., Pittalis, S., Puro, V., Capparuccia, M.R., Giombini, E., Gruber, C.E.M., Garbuglia, A.R., Marani, A., Vairo, F., Girardi, E., Vaia, F., Nicastri, E. and the INMI Monkeypox Group, 2022. Epidemiological, clinical and virological characteristics of four cases of monkeypox support transmission through sexual contact, Italy, May 2022. Eurosurveillance, 27(22), https://doi.org/10.2807/1560-7917.es.2022.27.22.2200421.
Sklenovská, N. and Van Ranst, M., 2018. Emergence of monkeypox as the most important orthopoxvirus infection in humans. Frontiers in Public Health, 6, https://doi.org/10.3389/fpubh.2018.00241.
Grant, R., Nguyen, L.-B.L. and Breban, R., 2020. Modelling human-to-human transmission of Monkeypox. Bulletin of the World Health Organization, 98(9), 638-640, https://doi.org/10.2471/blt.19.242347.
Angelo, K.M., Petersen, B.W., Hamer, D.H., Schwartz, E. and Brunette, G., 2019. Monkeypox transmission among international travellers—serious monkey business? Journal of Travel Medicine, 26(5), https://doi.org/10.1093/jtm/taz002.
Abdelaal, A., Serhan, H.A., Mahmoud, M.A., Rodriguez-Morales, A.J and Sah, R., 2023. Ophthalmic manifestations of Monkeypox virus. Eye, 37(3), 383-385, https://doi.org/10.1038/s41433-022-02195-z.
Response, E., 2023. Diagnostic Testing for the Monkeypox Virus (MPXV): Interim Guidance, 9 November 2023. [online] Available at: https://www.who.int/publications/i/item/who-mpx-laboratory-2023-1.
Ahsan, M.M., Gupta, K.D., Islam, M.M., Sen, S., Rahman, M.L. and Hossain, M.S., 2020. Covid-19 symptoms detection based on NasNetMobile with explainable AI using various imaging modalities. Machine Learning and Knowledge Extraction, 2(4), 490-504, https://doi.org/10.3390/make2040027.
Ahsan, M.M., Alam, T.E., Trafalis, T. and Huebner, P., 2020. Deep MLP-CNN model using mixed-data to distinguish between COVID-19 and non-covid-19 patients. Symmetry, 12(9), https://doi.org/10.3390/sym12091526.
Ahsan, M.M., Ahad, M.T., Soma, F.A., Paul, S., Chowdhury, A., Luna, S.A. and Huebner, P., 2021. Detecting SARS-COV-2 from chest x-ray using artificial intelligence. IEEE Access, 9, 35501-35513, https://doi.org/10.1109/access.2021.3061621.
Ahsan, M.M., Nazim, R., Siddique, Z. and Huebner, P., 2021. Detection of COVID-19 patients from CT scan and chest X-ray data using modified mobilenetv2 and lime. Healthcare, 9(9), https://doi.org/10.3390/healthcare9091099.
Lecun, Y., Bottou, L., Bengio, Y. and Haffner, P., 2019. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324, https://doi.org/10.1109/5.726791.
Jullapak, R. and Yampaka, T., 2021. Covid-19 classification using DCNNs and exploration correlation using canonical correlation analysis. Proceedings of the 18th International Joint Conference on Computer Science and Software Engineering (JCSSE), Thammasat University, Lampang Campus, Thailand, June 30-July 3, 2021, pp. 1-6, https://doi.org/10.1109/jcsse53117.2021.9493846.
Yampaka, T., Vonganansup, S. and Labcharoenwongs, P., 2022. Feature selection using regression mutual information deep convolution neuron networks for covid-19 x-ray image classification. International Journal of Advances in Intelligent Informatics, 8(2), https://doi.org/10.26555/ijain.v8i2.809.
Shorten, C. and Khoshgoftaar, T.M., 2019. A survey on image data augmentation for deep learning. Journal of Big Data, 6, https://doi.org/10.1186/s40537-019-0197-0.
Ahsan, M.M., Uddin, M.R., Farjana, M., Sakib, A.N., Momin, K.A. and Luna, S.A., 2022. Image data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16. arXiv, https://doi.org/10.48550/arxiv.2206.01862.
Islam, T., Hussain, M.A., Chowdhury, F.U.H. and Islam, B.M.R., 2022. Can artificial intelligence detect monkeypox from digital skin images? BioRxiv Preprint, https://doi.org/10.1101/2022.08.08.503193.
Sitaula, C. and Shahi, T.B., 2022. Monkeypox virus detection using pre-trained deep learning-based approaches. Journal of Medical Systems, 46(11), https://doi.org/10.1007/s10916-022-01868-2.
Park, A., 2022. There’s Already a Monkeypox Vaccine. But Not Everyone May Need It. [online] Available at: https://time.com/6179429/monkeypox-vaccine.
Ali, S.N., Ahmed, M.T., Jahan, T., Paul, J., Sani, S.M.S., Noor, N., Asma, A.N. and Hasan, T., 2023. A web-based Mpox skin lesion detection system using state-of-the-art deep learning models considering racial diversity. arXiv Preprint, https://doi.org/10.48550/arXiv.2306.14169.
Petersen, BW. and Damon, I.K., 2020. Smallpox, monkeypox, and other poxvirus infections. In: L. Goldman and A.I. Schafer, eds. Goldman-Cecil Medicine. 26th ed. Philadelphia, PA: Elsevier.
Moore, Z. S., 2008. Monkeypox. In: S.S. Long, ed. Principles and Practice of Pediatric Infectious Disease. 3rd ed. Amsterdam: Elsevier
Altindis, M., Puca, E. and Shapo, L., 2022. Diagnosis of monkeypox virus – an overview. Travel Medicine and Infectious Disease, 50, https://doi.org/10.1016/j.tmaid.2022.102459.
Narin, A., Kaya, C. and Pamuk, Z., 2021. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Analysis and Applications, 24(3), 1207-1220, https://doi.org/10.1007/s10044-021-00984-y.
Bala, D, 2022. Monkeypox Skin Images Dataset (MSID). [online] Available at: https://www.kaggle.com/datasets/dipuiucse/monkeypoxskinimagedataset.
Tan, M. and Le, Q.V., 2019. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. [online] Available at: https://arxiv.org/pdf/1905.11946.pdf.
Afzaal, H., Farooque, A.A., Schumann, A.W., Hussain, N., McKenzie-Gopsill, A., Esau, T., Abbas, F. and Acharya, B., 2021. Detection of a potato disease (early blight) using artificial intelligence. Remote Sensing, 13(3), https://doi.org/10.3390/rs13030411.
Simonyan, K. and Zisserman, A., 2014. Very Deep Convolutional Networks for Large-scale Image Recognition. [online] Available at: https://arxiv.org/pdf/1409.1556.pdf.
Bianco, S., Cadene, R., Celona, L. and Napoletano, P., 2018.Benchmark analysis of representative deep neural network architectures. IEEE Access, 6, 64270-64277, https://doi.org/10.1109/access.2018.2877890.