Monkeypox Detection Through Watershed Segmentation and Appending 2D CNN Based Auto Encoder Monkeypox Detection Through CNN-Auto Encoder

Article Sidebar

PDF
Published: Aug 31, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i9s.7472
Keywords:
Segmentation, Encoder, Watershed, convolutional neural network, Monkeypox

Main Article Content

Krishnan T
Research Scholar, Department of Information Technology, Faculty of Engineering and technology, Annamalai University, Annamalainagar-608002, Chidambaram
Selvakumar K
Head and Professor, Dept of Information Technology, Faculty of Engineering and Technology, Annamalai University, Annamalainagar-608002
Vairachilai S
Assistant Professor Senior Grade I, School of Computing Science and Engineering (SCSE), VIT Bhopal University

Abstract

Monkeypox, a viral zoonosis, may spread from animals to people. Fever, rashes, and swollen lymph nodes might create medical complications. Its symptoms resemble smallpox. To prevent monkey pox sickness, you must be prepared and treat it immediately. Public health systems should be aware of effective monkeypox mitigation methods because to its global health impacts. Watershed segmentation using CNN-based auto encoder detected monkeypox. Monkeypox may be distinguished from other skin infections. Watershed segmentation, elevation map utilisingsobel, and region-based feature extraction function well on impacted skin photos. Segmenting Monekypox images is tough due to similarities and variations across classes and the difficulties of focusing on skin lesions. Unsupervised learning models like the convolutional autoencoder duplicate the input image in the output layer. Encoders, ConvNets that produce low-dimensional images, process images passed via them.

Article Details

How to Cite
T, K. ., K, S. ., & S, V. . (2023). Monkeypox Detection Through Watershed Segmentation and Appending 2D CNN Based Auto Encoder: Monkeypox Detection Through CNN-Auto Encoder. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9s), 598–606. https://doi.org/10.17762/ijritcc.v11i9s.7472
Issue
Vol. 11 No. 9s (2023)
Section
Articles

References

. Adnan N, Haq ZU, Malik A, Mehmood A, Ishaq U, Faraz M, Malik J, Mehmoodi A. Human monkeypox virus: An updated review. Medicine (Baltimore). 2022 Sep 2;101(35):e30406. doi: 10.1097/MD.0000000000030406. PMID: 36107544; PMCID: PMC9439836.

. Ladnyj, I. D., Ziegler, P., & Kima, E. (1972). A human infection caused by monkeypox virus in Basankusu Territory, Democratic Republic of the Congo. Bulletin of the World Health Organization, 46(5), 593–597.

. Jezek, Z., Szczeniowski, M., Paluku, K. M., Mutombo, M., & Grab, B. (1988). Human monkeypox: confusion with chickenpox. Actatropica, 45(4), 297–307.

. 1. Larkin H (2023) New Report on Mpox Cases Leading Up to 2022 Global Outbreak. JAMA 329:531. doi: 10.1001/jama.2023.0744

. Sitaula, C., Shahi, T.B. Monkeypox Virus Detection Using Pre-trained Deep Learning-based Approaches. J Med Syst 46, 78 (2022). https://doi.org/10.1007/s10916-022-01868-2

. A. K. Gairola and V. Kumar, "Monkeypox Disease Diagnosis using Machine Learning Approach," 2022 8th International Conference on Signal Processing and Communication (ICSC), Noida, India, 2022, pp. 423-427, doi: 10.1109/ICSC56524.2022.10009135.

. Mohd. Tajuddin, Kabeer M., Mohammed Misbahuddin. (2023). Stress Detection Methods Using Hybrid Ontology through Social Media: Psychological Concerns. International Journal of Intelligent Systems and Applications in Engineering, 11(3s), 337–355. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2744

. Torky, Mohamed &Bakry, Mohamed &Hassanien, Aboul. (2022). Deep learning Model for Recognizing Monkey Pox based on Dense net-121 Algorithm. 10.1101/2022.12.20.22283747.

. Manohar, B., & Das, R. (2022). Artificial Neural Networks for the Prediction of Monkeypox Outbreak. Tropical Medicine and Infectious Disease, 7(12), 424. https://doi.org/10.3390/tropicalmed7120424

. Abdelhamid, A. A., El-Kenawy, E. M., Khodadadi, N., Mirjalili, S., Khafga, D., Alharbi, A. H., Ibrahim, A., Eid, M. M., & Saber, M. (2022b). Classification of Monkeypox Images Based on Transfer Learning and the Al-Biruni Earth Radius Optimization Algorithm. Mathematics, 10(19), 3614. https://doi.org/10.3390/math10193614

. Gul, I., Liu, C., Yuan, X., Du, Z., Zhai, S., Lei, Z., Chen, Q., Raheem, M. A., He, Q., Hu, Q., Xiao, C., Haihui, Z., Wang, R., Han, S., Du, K., Yu, D., Zhang, C. Y., & Qin, P. (2022b). Current and Perspective Sensing Methods for Monkeypox Virus. Bioengineering, 9(10), 571. https://doi.org/10.3390/bioengineering9100571

. Mwangi, J., Cohen, D., Costa, R., Min-ji, K., & Suzuki, H. Optimizing Neural Network Architecture for Time Series Forecasting. Kuwait Journal of Machine Learning, 1(3). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/132

. Ahsan, M., Uddin, M. J., Farjana, M., Sakib, A. N., Momin, K. A., & Luna, S. A. (2022b). Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16. Cornell University - ArXiv. https://doi.org/10.48550/arxiv.2206.01862

. Mandal, A. K. (2023). Usage of Particle Swarm Optimization in Digital Images Selection for Monkeypox Virus Prediction and Diagnosis. Research Square - Research Square. https://doi.org/10.21203/rs.3.rs-2421266

. Usman, S., & Adamu, I. M. (2017). Modeling the Transmission Dynamics of the Monkeypox Virus Infection with Treatment and Vaccination Interventions. Journal of Applied Mathematics and Physics, 05(12), 2335–2353. https://doi.org/10.4236/jamp.2017.512191

. Sahin, V. H., Oztel, I., &Oztel, G. Y. (2022b). Human Monkeypox Classification from Skin Lesion Images with Deep Pre-trained Network using Mobile Application. Journal of Medical Systems, 46(11). https://doi.org/10.1007/s10916-022-01863-7

. Qureshi, M., Khan, S., Bantan, R. a. R., Daniyal, M., Elgarhy, M., Marzo, R. R., & Lin, Y. (2022c). Modeling and Forecasting Monkeypox Cases Using Stochastic Models. Journal of Clinical Medicine, 11(21), 6555. https://doi.org/10.3390/jcm11216555

. Isaac N. Bankman, In Biomedical Engineering, Handbook of Medical Imaging, Academic Press, 2000, Pages 895-901, ISBN 9780120777907, https://doi.org/10.1016/B978-0-12-077790-7.50064-3 (https://www.sciencedirect.com/science/article/pii/B9780120777907500643)

. Ali, S. N., Ahmed, M. T., Paul, J., Jahan, T., Sani, S. M. Sakeef, Noor, N., & Hasan, T. (2022). Monkeypox Skin Lesion Detection Using Deep Learning Models: A Preliminary Feasibility Study. arXiv preprint arXiv:2207.03342.

. Mr. Rahul Sharma. (2018). Monitoring of Drainage System in Urban Using Device Free Localization Neural Networks and Cloud computing. International Journal of New Practices in Management and Engineering, 7(04), 08 - 14. https://doi.org/10.17762/ijnpme.v7i04.69

. Islam, M. Z., Islam, M. M., &Asraf, A. (2020). A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Informatics in medicine unlocked, 20, 100412. https://doi.org/10.1016/j.imu.2020.100412

. Altun M, Gürüler H, Özkaraca O, et al (2023) Monkeypox Detection Using CNN with Transfer Learning. Sensors 23:1783. https://doi.org/10.3390/s23041783

. Liu, Wenjie & Wang, Lu. (2022). Quantum image edge detection based on eight-direction Sobel operator for NEQR. Quantum Information Processing. 21. 10.1007/s11128-022-03527-4.