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

Anahtar Kelimeler:

VggNet, GoogLeNet, MobileNet, EfficientNet

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

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,

PDF

___

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