Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 5 , Issue 2 , PP: 47-57, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmed Islam 1 * , Mohamed G. Abdelfattah 2 , El-Sayed M. El-Kenawy 3 , Hossam El-Din Moustafa 4
Doi: https://doi.org/10.54216/JAIM.050205
Due to the importance of maintaining public health and preventing the spread of diseases, nowadays, new diseases have spread at a lot of countries called Monkeypox after the world get rid of covid-19.it is crucial to diagnose Monkeypox and stop the spread of this disease. so that we make this review to give a point of view to Monkeypox spread nowadays. We have recently done nine research to overlay it with different artificial intelligence deep learning methods to diagnose Monkeypox from digital skin images due primarily to AI's success in COVID-19 identification. The VGG16, VGG19, ResNet50, ResNet101, DenseNet201, and AlexNet models were used in our proposed method to classify patients with monkeypox symptoms with other diseases of a similar kind (chickenpox, measles, and normal)., Due to the importance of facing this disease and summarizing these researches according to: methodology and results of detection accuracy, precision.
artificial intelligence , Monkeypox , deep learning , Data collection , data augmentation.
[1] K.Chadaga, S.Prabhu, N.Sampathila, S.Nireshwalya, S.S.Katta, R.Tan, U.R.Acharya, Application of Artificial Intelligence Techniques for Monkeypox: A Systematic Review. MDPI,13(5), 824, 2023.
[2] Chadaga, Krishnaraj, Srikanth Prabhu, Niranjana Sampathila, Sumith Nireshwalya, Swathi S. Katta, Ru-San Tan, and U. Rajendra Acharya. "Application of artificial intelligence techniques for monkeypox: a systematic review." Diagnostics 13, no. 5 (2023): 824.
[3] Ahsan, Md Manjurul, Muhammad Ramiz Uddin, Md Shahin Ali, Md Khairul Islam, Mithila Farjana, Ahmed Nazmus Sakib, Khondhaker Al Momin, and Shahana Akter Luna. "Deep transfer learning approaches for Monkeypox disease diagnosis." Expert Systems with Applications 216 (2023): 119483.
[4] Jaradat, Ameera S., Rabia Emhamed Al Mamlook, Naif Almakayeel, Nawaf Alharbe, Ali Saeed Almuflih, Ahmad Nasayreh, Hasan Gharaibeh, Mohammad Gharaibeh, Ali Gharaibeh, and Hanin Bzizi. "Automated Monkeypox Skin Lesion Detection Using Deep Learning and Transfer Learning Techniques." International Journal of Environmental Research and Public Health 20, no. 5 (2023): 4422.
[5] Yasmin, Farhana, Md Mehedi Hassan, Mahade Hasan, Sadika Zaman, Chetna Kaushal, Walid El-Shafai, and Naglaa F. Soliman. "PoxNet22: A fine-tuned model for the classification of monkeypox disease using transfer learning." IEEE Access 11 (2023): 24053-24076.
[6] Sorayaie Azar, Amir, Amin Naemi, Samin Babaei Rikan, Jamshid Bagherzadeh Mohasefi, Habibollah Pirnejad, and Uffe Kock Wiil. "Monkeypox detection using deep neural networks." BMC Infectious Diseases 23, no. 1 (2023): 438.
[7] Almutairi, Saleh Ateeq. "DL-MDF-OH2: optimized deep learning-based monkeypox diagnostic framework using the metaheuristic Harris Hawks Optimizer Algorithm." Electronics 11, no. 24 (2022): 4077.
[8] Gupta, Aditya, Monu Bhagat, and Vibha Jain. "Blockchain-enabled healthcare monitoring system for early Monkeypox detection." The Journal of Supercomputing (2023): 1-25.
[9] M.Altun, H.Gürüler, O. Özkaraca, F.Khan, J.Khan, Y.Lee, Monkeypox Detection Using CNN with
Transfer Learning. MDPI, 23(4), 2023.
[10] M.M.Eid, E.L.El-Kenawy, N.Khodadadi, S.Mirjalili, E.Khodadadi, M.Abotaleb, A.H.Alharbi, A.A.Abdelhamid, A.Ibrahim, G.M.Amer, A.Kadi, D.S.Khafaga, Meta-Heuristic Optimization of LSTM-Based Deep Network for Boosting the Prediction of Monkeypox Cases. MDPI, 10(20), 2022.
[11] M.Haque, M.Ahmed, R.Nila, S.Islam, Classification of Human Monkeypox Disease Using Deep
Learning Models and Attention Mechanisms. arXiv preprint arXiv:2211.15459,2022.
[12] F.Yasmin, M.M.Hassan, M.Hasan,S.Zaman, C.Kaushl, W.El-Shafai, N.F.Soliman, PoxNet22: A Fine-Tuned Model for the Classification of Monkeypox Disease Using Transfer Learning. IEEE Access, 11, 24053-24076, 2023.
[13] F.Uysal, Detection of Monkeypox Disease from Human Skin Images with a Hybrid Deep Learning Model. Diagnostics, 13(10), 1772, 2023.
[21] V.H.Sahin, I.Oztel, G.Y.Oztel.Human Monkeypox Classification from Skin Lesion Images with Deep Pre-trained Network using Mobile Application, Journal of Medical Systems, 2022.
[22] A.S.Azar, A.Naemi, S.B.Rikan, J.B.Mohasefi, H.Pirnejad, U.K.Wiil, Monkeypox detection using deep neural networks. BMC Infectious Diseases, 23(1), 438, 2023.
[16] R. Pramanik, B. Banerjee, G. Efimenko, D. Kaplun, R. Sarkar, Monkeypox detection from skin lesion images using an amalgamation of CNN models aided with Beta function-based normalization scheme. Plos one, 18(4), 2023.
[17] M.Lakshmi, R.Das, Classification of Monkeypox Images Using LIME-Enabled Investigation of Deep Convolutional Neural Network. Diagnostics, 13(9), 1693, 2023.
[18] T.Nayak, K.Chadaga, N.Sampathila, H.Mayrose, G.M.Bairy,S.Prabhu, S.S.Katta, S.Umakanth, Detection of Monkeypox from skin lesion images using deep learning networks and explainable artificial intelligence. Applied Mathematics in Science and Engineering, 31(1), 2023
[19] D.Bala, M.S.Hassain, M.A.Hassain, M.I.Abdullah, M.M.Rahman, B.M.anavalan, N.Gu, M.S.Islam, Z.Huang, MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification. Neural Networks, 161, 757-775, 2023.
[20] A.A.Abdelhamid, E.M.El-kenawy, N.Khodadadi, S.Mirjalili, D.S.Khafaga, A.H.Alharbi, A.Ibrahim, M.M.Eid, M.Saber, Classification of Monkeypox Images Based on Transfer Learning and the Al-Biruni Earth Radius Optimization Algorithm. Mathematics, 2022.
[21] Images dataset (MSID) a new multiclass skin-based image datatset for Monkeypox disease detection – Kaggle [cited December 1, 2022]. Available from: https://www.kaggle.com/data sets/dipuiucse/monkeypoxskinimagedataset.
[22] Images dataset (MSLD) hosted in the Kaggle platform https://www.kaggle.com/ datasets/nafin59/Monkeypox-skin-lesion-dataset.
