Monkeypox Detection Using Hyper-Parameter Tuned Based Transferable CNN Model
Abstract
The reemergence of Monkeypox, a communicable illness resulting from the Monkeypox virus, has raised apprehensions about a potential swift global pandemic similar to the COVID-19 epidemic as COVID-19 infections diminish globally. The prompt emphasizes the criticality of prompt action within communities to mitigate the development of the phenomenon. The timely identification and accurate categorization of Monkeypox cutaneous manifestations are crucial for the successful implementation of containment strategies. This paper presents a novel methodology for detecting Monkeypox by utilizing a transferrable Convolutional Neural Network (CNN) model that has been optimized utilizing hyper-parameter tuning techniques. The proposed methodology initiates by improving the quality of the original Monkeypox images, with a specific emphasis on boosting edge details to increase visual clarity. Texture qualities are obtained through an energy layer, enhancing distinctive traits. Our methodology's cornerstone is utilizing the Hyper-parameter-based transferable Convolutional Neural Network (HPT-TCNN), specifically designed to enhance classification accuracy.
In contrast to traditional methods, we enhance the architectural design by replacing the pooling layer with a configuration comprising three convolutional layers and one energy layer. The hyper-parameter tuning procedure is optimized by employing the Optimisation Algorithm known as MGS-ROA. In order to enhance the process of model training and validation, we have assembled the "Monkeypox Skin Lesion Dataset (MSLD)," which consists of a collection of images depicting human skin lesions produced by Monkeypox. The dataset in question is vital in evaluating and improving our methodology. In a comparison analysis conducted on other deep learning models, the suggested model has superior performance compared to other models, obtaining a notable accuracy, sensitivity, and specificity, all reaching a value of 93.60%. The outstanding performance shown in this study highlights the methodology's effectiveness in adequately classifying skin lesions associated with Monkeypox. This approach shows potential for physicians and healthcare workers since it facilitates early detection, a crucial factor in preventing the spread of Monkeypox.
References
Adalja, A., & Inglesby, T. (2022). A Novel International Monkeypox Outbreak. Annals of Internal Medicine, 175(8), 1175-1176. https://doi.org/10.7326/M22-1581.
Altun, M., Gürüler, H., Özkaraca, O., Khan, F., Khan, J., & Lee, Y. (2023). Monkeypox Detection Using CNN with Transfer Learning. Sensors (Basel), 23(4), 1783. https://doi.org/10.3390/s23041783.
Almufareh MF., Tehsin S., Humayun M., & Kausar S. (2023). A Transfer Learning Approach for Clinical Detection Support of Monkeypox Skin Lesions. Diagnostics (Basel), 13(8), 1503. https://doi.org/10.3390/diagnostics13081503.
Ariansyah, M. H., Winarno, S., & Sani, R. R. (2023). Monkeypox and Measles Detection using CNN with VGG-16 Transfer Learning. Journal of Computing Research and Innovation, 8(1), 32-44. https://doi.org/10.24191/jcrinn.v8i1.340
Ayca, A. K., Vedat, T., Ipek, M. (2022). Motor imagery EEG signal classification using image processing technique over GoogLeNet deep learning algorithm for controlling the robot manipulator. Biomedical Signal Processing and Control, 72(A), 103295, ISSN: 1746-8094. https://doi.org/10.1016/j.bspc.2021.103295.
Binu, D., & Kariyappa, B. S. (2019). RideNN: A New Rider Optimization Algorithm-Based Neural Network for Fault Diagnosis in Analog Circuits. IEEE Transactions on Instrumentation and Measurement, 68(1), 2-26.
https://doi.oeg/10.1109/TIM.2018.2836058.
Català, A., Clavo-Escribano, P., Riera-Monroig, J., Martín-Ezquerra, G., Fernandez-Gonzalez, P., … & Fuertes, I. (2022). Monkeypox outbreak in Spain: clinical and epidemiological findings in a prospective cross-sectional study of 185 cases. The British Journal of Dermatology, 187(5), 765-772. https://doi.org/10.1111/bjd.21790.
Chollet, F. (2018). Keras: The python deep learning library. Astrophys. Source Code Library, Tech. Rep. Rec. Ascl:1806.022.
Jadhav, A. S., Patil, P. B., & Biradar, S. (2021). Optimal feature selection-based diabetic retinopathy detection using improved rider optimization algorithm enabled with deep learning. Evolutionary intelligence, 14(4), 1431-1448. https://doi.org/10.1007/s12065-020-00400-0
Khalifa, N. E., Loey, M., & Mirjalili, S. (2022). A comprehensive survey of recent trends in deep learning for digital images augmentation. Artificial Intelligence Review, 55(3), 2351-2377. https://doi.org/10.1007/s10462-021-10066-4.
Li, D. & Ling, D. (2021). Recent advances of deep learning algorithms for aquacultural machine vision systems with emphasis on fish. Artificial Intelligence Review. 55(4). https://doi.org/10.1007/s10462-021-10102-3.
Nolen, L. D., Osadebe, L., Katomba, J., Likofata, J., Mukadi, D., … & Reynolds, M. G. (2016). Extended Human-to-Human Transmission during a Monkeypox Outbreak in the Democratic Republic of the Congo. Emerging Infectious Diseases, 22(6), 1014-1021. https://doi.org/10.3201/eid2206.150579.
Out, A., Ebenso, B., Walley, J., Barceló, J. M., & Ochu, C. L. (2022). Global human Monkeypox outbreak: atypical presentation demanding urgent public health action. The Lancet, Microbe, 3(8), e554-e555. https://doi.org/10.1016/S2666-5247(22)00153-7.
Pramanik, R., Banerjee, B., Efimenko, G., Kaplun, D., & Sarkar, R. (2023). Monkeypox detection from skin lesion images using an amalgamation of CNN models aided with Beta function-based normalization scheme. PLoS One, 18(4), e0281815. https://doi.org/10.1371/journal.pone.0281815.
Samaranayake, L., & Anil, S. (2022). The Monkeypox Outbreak and Implications for Dental Practice. International Dental Journal, 72(5), 589-596. https://doi.org/10.1016/j.identj.2022.07.006.
Sanaat, A., Shiri, I., Ferdowsi, S., Arabi, H., & Zaidi H. (2022). Robust-Deep: A Method for Increasing Brain Imaging Datasets to Improve Deep Learning Models’ Performance and Robustness. Journal of Digital Imaging, 35, 469–481. https://doi.org/10.1007/s10278-021-00536-0.
Savaş, Serkan. (2022). Detecting the Stages of Alzheimer’s Disease with Pre-trained Deep Learning Architectures. Arabian Journal for Science and Engineering, 47(2), 2201-2218. https://doi.org/10.1007/s13369-021-06131-3.
Suganyadevi, S., Seethalakshmi, V., & Balasamy, K. (2022). A review on deep learning in medical image analysis. International Journal of Multimedia Information Retrieval, 11(1), 19-38. https://doi.org/10.1007/s13735-021-00218-1.
Uysal, F. (2023). Detection of Monkeypox Disease from Human Skin Images with a Hybrid Deep Learning Model. Diagnostics (Basel), 13(10), 1772. https://doi.org/10.3390/diagnostics13101772.
Yasmin, F., Hassan, M. M., Hasan, M., Zaman, S., Kaushal, C., El-Shafai, W., & Soliman, N. F. (2023). PoxNet22: A fine-tuned model for the classification of Monkeypox disease using transfer learning. IEEE Access, 11, 24053-24076. https://doi.org/10.1109/ACCESS.2023.3253868.
Yue, X., Li, H., Shimizu, M., Kawamura, S., & Meng, L. (2022). YOLO-GD: A Deep Learning-Based Object Detection Algorithm for Empty-Dish Recycling Robots. Machines, 10(5), 294. http://dx.doi.org/10.3390/machines10050294
Zhang, J., Li, C., Rahaman, M. M., Yao, Y., & Ma, P. (2021). A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches. Artificial Intelligence Review, 55(4), 2875-2944. https://doi.org10.1007/s10462-021-10082-4.
Zumla A., Valdoleiros, S.R., Haider, N., Asogun, D., & Ntoumi, Petersen, E., & Kock, R. (2022). Monkeypox outbreaks outside endemic regions: scientific and social priorities. The Lancet Infectious Diseases, 22(7), 929-931. https://doi.org/10.1016/S1473-3099(22)00354-1.
Copyright (c) 2023 International Academic Publishing House (IAPH)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
