Detection of Monkeypox Among Different Pox Diseases with Different Pre-Trained Deep Learning Models

Year 2023, , 10 - 21, 01.03.2023

Muhammed Çelik , Özkan İnik

https://doi.org/10.21597/jist.1206453

Cited By: 1

Abstract

Monkeypox is a viral disease that has recently rapidly spread. Experts have trouble diagnosing the disease because it is similar to other smallpox diseases. For this reason, researchers are working on artificial intelligence-based computer vision systems for the diagnosis of monkeypox to make it easier for experts, but a professional dataset has not yet been created. Instead, studies have been carried out on datasets obtained by collecting informal images from the Internet. The accuracy of state-of-the-art deep learning models on these datasets is unknown. Therefore, in this study, monkeypox disease was detected in cowpox, smallpox, and chickenpox diseases using the pre-trained deep learning models VGG-19, VGG-16, MobileNet V2, GoogLeNet, and EfficientNet-B0. In experimental studies on the original and augmented datasets, MobileNet V2 achieved the highest classification accuracy of 99.25% on the augmented dataset. In contrast, the VGG-19 model achieved the highest classification accuracy with 78.82% of the original data. Considering these results, the shallow model yielded better results for the datasets with fewer images. When the amount of data increased, the success of deep networks was better because the weights of the deep models were updated at the desired level.

Keywords

Monkeypox, VggNet, GoogLeNet, MobileNet, EfficientNet

References

• Ahsan, M. M., Uddin, M. R., Farjana, M., Sakib, A. N., Momin, K. al, & Luna, S. A. (2022). Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16. https://doi.org/10.48550/arxiv.2206.01862
• Alenezi, F., Armghan, A., & Polat, K. (2023). Wavelet transform based deep residual neural network and ReLU based Extreme Learning Machine for skin lesion classification. Expert Systems with Applications, 213, 119064. https://doi.org/10.1016/J.ESWA.2022.119064
• Ali, S. N., Ahmed, Md. T., Paul, J., Jahan, T., Sani, S. M. S., Noor, N., & Hasan, T. (2022). Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study. https://doi.org/10.48550/arxiv.2207.03342
• Aljaddouh, B., & Malathi, D. (2022). Trends of using machine learning for detection and classification of respiratory diseases: Investigation and analysis. Materials Today: Proceedings, 62, 4651–4658. https://doi.org/10.1016/J.MATPR.2022.03.120
• Bayat, S., & Işik, G. (2022). Aras Kuş Türlerinin Ses Özellikleri Bakımından Derin Öğrenme Yöntemleriyle Tanınması. Journal of the Institute of Science and Technology, 12(3), 1250–1263. https://doi.org/10.21597/JIST.1124674
• Bhatt, H., Shah, V., Shah, K., Shah, R., & Shah, M. (2022). State-of-the-art machine learning techniques for melanoma skin cancer detection and classification: a comprehensive review. Intelligent Medicine. https://doi.org/10.1016/J.IMED.2022.08.004
• Bhattacharjee, S., Saha, B., Bhattacharyya, P., & Saha, S. (2022). Classification of obstructive and non-obstructive pulmonary diseases on the basis of spirometry using machine learning techniques. Journal of Computational Science, 63, 101768. https://doi.org/10.1016/J.JOCS.2022.101768
• 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. https://doi.org/10.1371/JOURNAL.PNTD.0010141
• CDC. (n.d.). 2022 Outbreak Cases and Data | Monkeypox | Poxvirus | CDC. Retrieved October 30, 2022, from https://www.cdc.gov/poxvirus/monkeypox/response/2022/index.html
• Elashiri, M. A., Rajesh, A., Nath Pandey, S., Kumar Shukla, S., Urooj, S., & Lay-Ekuakille, A. (2022). Ensemble of weighted deep concatenated features for the skin disease classification model using modified long short term memory. Biomedical Signal Processing and Control, 76, 103729. https://doi.org/10.1016/J.BSPC.2022.103729
• Ferreira, M. I. A. S. N., Barbieri, F. A., Moreno, V. C., Penedo, T., & Tavares, J. M. R. S. (2022). Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters. Gait & Posture, 98, 49–55. https://doi.org/10.1016/J.GAITPOST.2022.08.014
• Hu, Y., Wen, C., Cao, G., Wang, J., & Feng, Y. (2022). Brain network connectivity feature extraction using deep learning for Alzheimer’s disease classification. Neuroscience Letters, 782, 136673. https://doi.org/10.1016/J.NEULET.2022.136673
• Ibrahim, D. M., Elshennawy, N. M., & Sarhan, A. M. (2021). Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases. Computers in Biology and Medicine, 132, 104348. https://doi.org/10.1016/J.COMPBIOMED.2021.104348
• Inik, Ö., & Turan, B. (2018). Classification of Different Age Groups of People by Using Deep Learning. https://www.researchgate.net/publication/333045149
• Inik, O., Uyar, K., & Ülker, E. (2019). Gender Classification with A Novel Convolutional Neural Network (CNN) Model and Comparison with other Machine Learning and Deep Learning CNN Models. https://www.researchgate.net/publication/330279739
• Islam, T., Hussain, M. A., Uddin, F., Chowdhury, H., & Islam, B. M. R. (2022). Can Artificial Intelligence Detect Monkeypox from Digital Skin Images? BioRxiv, 2022.08.08.503193. https://doi.org/10.1101/2022.08.08.503193
• Jia, Z., & Chen, D. (2020). Brain Tumor Identification and Classification of MRI images using deep learning techniques. IEEE Access, 1–1. https://doi.org/10.1109/ACCESS.2020.3016319
• Krizhevsky, A., & Inc, G. (2014). One weird trick for parallelizing convolutional neural networks. https://doi.org/10.48550/arxiv.1404.5997
• Li, Y., Luo, J. hao, Dai, Q. yun, Eshraghian, J. K., Ling, B. W. K., Zheng, C. yan, & Wang, X. li. (2023). A deep learning approach to cardiovascular disease classification using empirical mode decomposition for ECG feature extraction. Biomedical Signal Processing and Control, 79, 104188. https://doi.org/10.1016/J.BSPC.2022.104188
• Memariani, M., & Memariani, H. (2022). Multinational monkeypox outbreak: what do we know and what should we do? Irish Journal of Medical Science (1971 -) 2022, 1–2. https://doi.org/10.1007/S11845-022-03052-4
• Monisha, M., Suresh, A., & Rashmi, M. R. (2018). Artificial Intelligence Based Skin Classification Using GMM. Journal of Medical Systems, 43(1), 1–8. https://doi.org/10.1007/S10916-018-1112-5/FIGURES/12
• WHO. (n.d.). Monkeypox. Retrieved October 30, 2022, from https://www.who.int/news-room/fact-sheets/detail/monkeypox monkeypox 2022 remastered | Kaggle. (n.d.). Retrieved November 5, 2022, from https://www.kaggle.com/datasets/maxmelichov/monkeypox-2022-remastered
• Muñoz-Saavedra, L., Escobar-Linero, E., Civit-Masot, J., Luna-Perejón, F., Civit, A., & Domínguez-Morales, M. (2022). Monkeypox Diagnostic-Aid System with Skin Images Using Convolutional Neural Networks. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.4186534
• Nguyen, D., Nguyen, H., Ong, H., Le, H., Ha, H., Duc, N. T., & Ngo, H. T. (2022). Ensemble learning using traditional machine learning and deep neural network for diagnosis of Alzheimer’s disease. IBRO Neuroscience Reports, 13, 255–263. https://doi.org/10.1016/J.IBNEUR.2022.08.010
• Pacal, İ. (2022). Deep Learning Approaches for Classification of Breast Cancer in Ultrasound (US) Images. Journal of the Institute of Science and Technology, 12(4), 1917–1927. https://doi.org/10.21597/JIST.1183679
• Pacal, I., & Karaboga, D. (2021). A robust real-time deep learning based automatic polyp detection system. Computers in Biology and Medicine, 134, 104519. https://doi.org/10.1016/J.COMPBIOMED.2021.104519
• Pacal, I., Karaboga, D., Basturk, A., Akay, B., & Nalbantoglu, U. (2020). A comprehensive review of deep learning in colon cancer. Computers in Biology and Medicine, 126, 104003. https://doi.org/10.1016/J.COMPBIOMED.2020.104003
• Parker, S., & Buller, R. M. (2013). A review of experimental and natural infections of animals with monkeypox virus between 1958 and 2012. Http://Dx.Doi.Org/10.2217/Fvl.12.130, 8(2), 129–157. https://doi.org/10.2217/FVL.12.130
• Qian, S., Ren, K., Zhang, W., & Ning, H. (2022). Skin lesion classification using CNNs with grouping of multi-scale attention and class-specific loss weighting. Computer Methods and Programs in Biomedicine, 226, 107166. https://doi.org/10.1016/J.CMPB.2022.107166
• Rezaee, K., Savarkar, S., Yu, X., & Zhang, J. (2022). A hybrid deep transfer learning-based approach for Parkinson’s disease classification in surface electromyography signals. Biomedical Signal Processing and Control, 71, 103161. https://doi.org/10.1016/J.BSPC.2021.103161
• Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 4510–4520. https://doi.org/10.48550/arxiv.1801.04381
• Shinde, A. S., Mahendra, B., Nejakar, S., Herur, S. M., & Bhat, N. (2022). Performance analysis of machine learning algorithm of detection and classification of brain tumor using computer vision. Advances in Engineering Software, 173, 103221. https://doi.org/10.1016/J.ADVENGSOFT.2022.103221
• Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. https://doi.org/10.48550/arxiv.1409.1556
• Swathy, M., & Saruladha, K. (2022). A comparative study of classification and prediction of Cardio-Vascular Diseases (CVD) using Machine Learning and Deep Learning techniques. ICT Express, 8(1), 109–116. https://doi.org/10.1016/J.ICTE.2021.08.021
• Talukder, M. A., Islam, M. M., Uddin, M. A., Akhter, A., Hasan, K. F., & Moni, M. A. (2022). Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning. Expert Systems with Applications, 205, 117695. https://doi.org/10.1016/J.ESWA.2022.117695
• Tan, M., & Le, Q. v. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. 36th International Conference on Machine Learning, ICML 2019, 2019-June, 10691–10700. https://doi.org/10.48550/arxiv.1905.11946
• Vankdothu, R., & Hameed, M. A. (2022). Brain tumor segmentation of MR images using SVM and fuzzy classifier in machine learning. Measurement: Sensors, 24, 100440. https://doi.org/10.1016/J.MEASEN.2022.100440
• Vuidel, A., Cousin, L., Weykopf, B., Haupt, S., Hanifehlou, Z., Wiest-Daesslé, N., Segschneider, M., Lee, J., Kwon, Y.-J., Peitz, M., Ogier, A., Brino, L., Brüstle, O., Sommer, P., & Wilbertz, J. H. (2022). High-content phenotyping of Parkinson’s disease patient stem cell-derived midbrain dopaminergic neurons using machine learning classification. Stem Cell Reports, 17(10), 2349–2364. https://doi.org/10.1016/J.STEMCR.2022.09.001
• Wei, Z., Li, Q., & Song, H. (2022). Dual attention based network for skin lesion classification with auxiliary learning. Biomedical Signal Processing and Control, 74, 103549. https://doi.org/10.1016/J.BSPC.2022.103549
• Xin, C., Liu, Z., Zhao, K., Miao, L., Ma, Y., Zhu, X., Zhou, Q., Wang, S., Li, L., Yang, F., Xu, S., & Chen, H. (2022). An improved transformer network for skin cancer classification. Computers in Biology and Medicine, 149, 105939. https://doi.org/10.1016/J.COMPBIOMED.2022.105939
• Zhou, J., Wu, Z., Jiang, Z., Huang, K., Guo, K., & Zhao, S. (2022). Background selection schema on deep learning-based classification of dermatological disease. Computers in Biology and Medicine, 149, 105966. https://doi.org/10.1016/J.COMPBIOMED.2022.105966