Monkeypox detection and classification using multi-layer convolutional neural network from skin images

M. Laxman Rao; A. Venkata Mahesh; Manne Naga V. J. Manikanth; Juvvala Sailaja; Kovvuri N. Bhargavi

Author(s)

M. Laxman Rao Computer Science and Systems Engineering, Andhra University,Visakhapatnam,India

A. Venkata Mahesh Department of Computer Science and Engineering, GIET UNIVERSITY,Gunupur, Orissa,India

Manne Naga V. J. Manikanth Department of Information Technology and Computer Applications, Andhra University, Visakhapatnam, India

Juvvala Sailaja Department of Information Technology, Aditya College of Engineering and Technology, Surampalem, India

Kovvuri N. Bhargavi Department of Information Technology, Aditya College of Engineering and Technology, Surampalem, India

Abstract

The latest pandemic of monkeypox is a significant cause for worry for the public's health due to the rapidity with which it has spread to more than 40 nations outside of Africa. When monkeypox is so like both measles and chickenpox, making an accurate clinical diagnosis of the disease may be difficult. The monitoring and early identification of suspected cases of monkeypox may benefit from computer-assisted detection of lesions. This is particularly true in environments where confirmatory Polymerase Chain Reaction (PCR) assays are not easily accessible. It has been shown that it is possible to do automated skin lesion identification via deep learning (DL) approaches given sufficient training instances. However, it is expected that these procedures will be followed. However, there are currently no datasets of this sort available for monkeypox. Focusing on forecasting monkeypox disease from skin pictures, this study focuses on developing a transfer learning-based multi-layer convolutional neural network (MLCNN) algorithm. Through pre-processing, we can ensure that all the images are of the same quality and that any distracting sounds have been eliminated. The simulation results showed that the proposed MLCNN outperformed the conventional model, proving the validity of the proposed approach. The MLCNN resulted in an accuracy is 99.1, precision is 99.1%, recall is 99.1%, and F1-score is 99.1%.

Publication Details

Page(s) 2364-2379

DOI DOI not available

Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 18, Issue: 21, Year: 2023

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Keywords

chain reaction, Monkeypox detection, multi-layer MLCNN, polymerase skin images

References

Sitaula C.,Shahi T. B. .2022 .Monkeypox virus detection using pre-trained deep learning-based approaches. Journal of Medical Systems, 46(11) : 78.

Cohen J. P.,Morrison P.,Dao L.,Roth K.,Duong T. Q.,Ghassemi M. .2006 .Covid-19 image data collection: Prospective predictions are the future. , : .

Luna-Perejón F.,Domínguez-Morales M.,Civit-Balcells GutiérrezGalán D.,A. GutiérrezGalán D. .2020 .Low-power embedded system for gait classification using neural networks. Journal of Low Power Electronics and Applications, 10(2) : 14.

Ali S. N.,Ahmed M. T.,Jahan Paul J.,Sani T.,Noor S. M.,Hasan N.,T. N. .2022 .Monkeypox skin lesion detection using deep learning models: A feasibility study. arXiv preprint arXiv:2207, : 03342.

Ahsan M. M.,Uddin M. R.,Luna S. A. .2022 .Monkeypox image data collection. arXiv preprint arXiv:2206, : 01774.

Nguyen P. Y.,Ajisegiri W. S.,Costantino V.,Chughtai A. A.,MacIntyre C. R. A. A. .2021 .Reemergence of human monkeypox and declining population immunity in the context of urbanization. , 27(4) : 1007-2020.

Ahsan M. M.,Nazim R.,Z. R. .2021 .August. Detection of COVID-19 patients from CT scan and chest X-ray data using modified MobileNetV2 and LIME. , 9(9) : 1099.

Menzies T.,Greenwald J.,Frank A. .2006 .Data mining static code attributes to learn defect predictors. IEEE transactions on software engineering, 33(1) : 2-13.

Wang Linda,Wong Alexander .2020 .Covid-net: A tailored deep MLCNN design for detection of covid-19 cases from chest x-ray images. Scientific Reports, 10(1) : 1-12.

Pan P.,Li Y.,Xiao Y,Han B.,Su L.,Su M.,Li Y.,Zhang S.,Jiang D.,Chen X.,Zhou F. .2020 .Prognostic assessment of COVID-19 in the intensive care unit by machine learning methods: model development and validation. Journal of medical Internet research, 22(11) : e23128.

Gul I.,Liu C.,Yuan X.,Zhai Z.,S. Z.,Chen Z.,Raheem Q.,He M. A.,Hu Q.,Xiao Q.,C. Q. .2022 .. , : .

de Jonge E. F.,Peterse C. M.,Koelewijn J. M.,van der Drift A. M. R van der Beek R. F.,Nagelkerke E.,Lodder W.J. .2022 .The detection of monkeypox virus DNA in wastewater samples in the Netherlands. Science of the Total Environment, 852 : 158265.

Ali S. N.,Ahmed M. T.,Jahan Paul J.,Sani T.,Noor S. M.,Hasan N.,T. N. .2022 .Monkeypox skin lesion detection using deep learning models: A feasibility study. arXiv preprint arXiv:2207, : 03342.

Wolfe M. K.,Duong D.,Hughes B.,Chan-Herur V.,White B.,Boehm A. .2022 .Detection of monkeypox viral DNA in a routine wastewater monitoring program. medRxiv, : 2022-07.

Hussain M. A.,Islam T.,Chowdhury F. U. H. and Islam B. R. .2022 .Can artificial intelligence detect Monkeypox from digital skin images? BioRxiv. , : 2022-08.

Ferré V. M.,Bachelard A.,Zaidi M.,Armand-Lefevre L.,Descamps D.,Charpentier C.,Ghosn J. .2022 .Detection of monkeypox virus in anorectal swabs from asymptomatic men who have sex with men in a sexually transmitted infection screening. Annals of Internal Medicine, 175(10) : 1491-1492.

Shapovalova V. .2022 .Monkeypox virus-new challenges of modernity: experimental organizational and legal, clinical and pharmacological studies. SSP Modern Pharmacy and Medicine, 2(3) : 1-15.

Sahin V. H.,Oztel I.,Yolcu Oztel G. I. .2022 .Human monkeypox classification from skin lesion images with deep pre-trained network using mobile application. Journal of Medical Systems, 46(11) : 79.

León-Figueroa D ,Barboza J. J.,Saldaña-Cumpa H. M. J. J.,Moreno-Ramos E.,Bonilla-Aldana D. K E.,Valladares-Garrido M. J. E.,Sah R.,RodriguezMorales A.J. R. .2022 .Detection of monkeypox virus according to the collection site of samples from confirmed cases: a systematic review. Tropical Medicine and Infectious Disease, 8(1) : 4.

Haider N.,Guitian J.,Simons D.,Asogun D.,Ansumana R.,Honeyborne I.,Velavan T. P.,Ntoumi F.,Valdoleiros S. R.,Petersen E.,Kock R. .2022 .Increased outbreaks of monkeypox highlight gaps in. , : .

Girón-Guzmán I.,Díaz-Reolid A.,Truchado P.,Carcereny A.,García-Pedemonte D.,Hernáez B.,Bosch A.,Pintó R. M.,Guix S.,Allende A.,Alcamí A. .2023 .Spanish wastewater reveals the current spread of Monkeypox virus. Water research, 231 : 119621.

Relhan V.,Sahay R. R.,Shete A. M.,Yadav P. D. A. M.,Sahoo B.,Patil D. Y.,Kumar S.,Premachandran Syamaladevi K. S. S.,Dar L.,Mohandas S.,Abraham P. .2023 .Clinical presentation, viral kinetics, and management of human monkeypox cases from New Delhi, India 2022. Journal of Medical Virology, 95(1) : e28249.

Wu F.,Oghuan J.,Gitter A.,Mena K. D. Brown E.L. .2023 .Wide mismatches in the sequences of primers and probes for monkeypox virus diagnostic assays. Journal of Medical Virology, 95(1) : e28395.

Liu R. .2022 .March. Convolutional Siamese networkbased few-shot learning for monkeypox detection under data scarcity. , 12611 : 1459-1465.

Ahsan M. M.,Uddin M. R.,Ali M. S.,Islam M. K.,Farjana M.,Sakib A. N.,Al Momin K.,Luna S. A. .2023 .Deep transfer learning approaches for Monkeypox disease diagnosis. Expert Systems with Applications, 216 : 119483.

Lai C. C.,Hsu C. K.,Yen M. Y.,Lee P. I.,Ko W. C.,Hsueh P. R. .2022 .Monkeypox: An emerging global threat during the COVID-19 pandemic. Journal of Microbiology, : .

Gessain A.,Nakoune E.,Yazdanpanah Y. .2022 .. New England Journal of Medicine, 387(19) : 1783-1793.

Gaspari V.,Rossini G.,Robuffo S.,Rapparini L.,Scagliarini A.,Mistral De A.,Piraccini B. M.,Lazzarotto T. .2023 .Monkeypox Outbreak 2022: Clinical and Virological Features of 30 Patients at the Sexually Transmitted Diseases Centre of Sant'Orsola Hospital. Journal of Clinical Microbiology, 61(1) : e01365-22.

Bhattacharya M.,Dhama K.,Chakraborty C. .2022 .Recently spreading human monkeypox virus infection and its transmission during COVID-19 pandemic period: A travelers prospective. Travel Medicine and Infectious Disease, 49 : 102398.

Thornhill J. P.,Barkati S.,Walmsley S.,Rockstroh J.,Antinori A.,Harrison L. B.,Palich R.,Nori A.,Reeves I.,Habibi M. S.,Apea V. .2022 .Monkeypox virus infection in humans across 16 countries-April-June 2022. , 387(8) : 679-691.

Jaradat A. S.,Almakayeel R. E.,Alharbe N.,Almuflih N.,Nasayreh A. S.,Gharaibeh A.,Gharaibeh H.,Gharaibeh M.,Bzizi A.,H. A. .2023 .Automated Monkeypox Skin Lesion Detection Using Deep Learning and Transfer Learning Techniques. International Journal of Environmental Research and Public Health, 20(5) : 4422.

Deshmukh P.,Vora A.,Tiwaskar M.,Joshi S. .2022 .Monkeypox: What do we know so far? A short narrative review of literature. J Assoc Physicians India, 70(7) : 11-12.

Bunge E. M.,Hoet B.,Chen L.,Lienert F.,Weidenthaler H.,Baer L. R.,Steffen R. .2022 .The changing epidemiology of human monkeypox-A potential threat? A systematic review. PLoS neglected tropical diseases, 16(2) : e0010141.

Koenig K. L.,Beÿ C. K.,Marty A. M. .2022 .Monkeypox 2022: a primer and identify-isolateinform (3I) tool for emergency medical services professionals. Prehospital and Disaster Medicine, 37(5) : 687-692.

Khafaga D. S.,Ibrahim A.,El-Kenawy E. S. M.,Abdelhamid A. A.,Karim F. K.,Mirjalili S.,Khodadadi N.,Lim W. H.,Eid M. M.,Ghoneim M. E. .2022 .An Al-Biruni earth radius optimizationbased deep convolutional neural network for classifying monkeypox disease. Diagnostics, 12(11) : 2892.

Thakur N. .2022 .Monkeypox2022tweets: a large-scale twitter dataset on the 2022 monkeypox outbreak. , : .

Barboza J. J.,León-Figueroa D J. J.,Saldaña-Cumpa H. M. J. J.,Valladares-Garrido M. J. J. J.,Moreno-Ramos E.,Sah R.,Bonilla-Aldana D. K R.,Rodriguez-Morales A. J. .2023 .Virus Identification for Monkeypox in Human Seminal Fluid Samples: A Systematic Review. Tropical Medicine and Infectious Disease, 8(3) : 173.

Sudarmaji N.,Kifli N.,Hermansyah A.,Yeoh S. F.,Goh B. H.,Ming L. C. .2022 .Prevention and treatment of monkeypox: a systematic review of preclinical studies. Viruses, 14(11) : 2496.

Dada E. G.,Oyewola D. O.,Joseph S. B.,Emebo O.,Oluwagbemi O. O. .2022 .Ensemble machine learning for monkeypox transmission time series forecasting. Applied Sciences, 12(23) : 12128.