Meta-Heuristic Based Melanoma Skin Disease Detection and Classification Using Wolf Antlion Neural Network (WALNN) Model

Authors

  • R. Rajeswari Associate Professor, Department of Computer Science, Dr MGR Educational and Research Institute, Chennai
  • K. Kalaiselvi Associate Professor, Department of Computer Applications, Saveetha College of Liberal Arts and Sciences, Saveetha Institute of Medical and Technical Sciences, Chennai
  • N. Jayashri Associate Professor, Faculty of Computer Applications, Dr. M.G.R Educational and Research Institute, Chennai
  • P. Lakshmi Assistant Professor in Computer Science, SRM Institute of Science and Technology, Tiruchirappalli Campus
  • A. Muthusamy Assistant Professor, Department of Computer Technology, Kongu Engineering College, Perundurai, Erode - 638060

Keywords:

Optimization, Feature Selection, Skin Melanoma, Deep Learning, Classifier

Abstract

Skin cancer is among the most prevalent cancers in people. Most diagnoses are made visually, with clinical screening, histological investigation, biopsy and optional dermoscopic steps. Melanoma identification in the skin has been described as problematic due to the possible issues resulting from inadequate feature selection and the importance of achieving high levels of detection accuracy. To classify the malignant or average level of skin melanoma from MRI images, the optimal Wolf AntLion Neural Network (WALNN) model as a meta-heuristic-based deep learning (DL) approach is used in this paper. Correspondingly, the novel hybrid algorithm, namely Wolf Pack Search and AntLion Optimization Algorithm Optimization, is developed for optimal feature selection to ensure the performance of the Convolutional Neural Network learning classifier. The proposed approach, WALNN, is evaluated with existing techniques such as Decision Tree (DT), Cuckoo Search and Support Vector Machine (CS-SVM), and Convolution Neural Network (CNN) using the ISIC archive skin lesion dataset. As a result, the proposed methodology is carried out with a better outcome in term of sensitivity, specificity, Precision, Recall, and Accuracy and perform the recognition precisely.

Downloads

Download data is not yet available.

References

Kostrzewska, Paulina, et al. "Sunscreens as a prevention of the photoaging." Journal of Education, Health and Sport 10.8 (2020): 11-16.

LobdellKevin, W., and A. RoseGeoffrey."Improving health care leadership in the COVID-19 era."NEJM Catalyst Innovations in Care Delivery (2020).

Igari, Fumie, Hisashi Tanaka, and Armando E. Giuliano. "The Applications of Plasma Cell-free DNA in Cancer Detection: Implications in the Management of Breast Cancer Patients." Critical Reviews in Oncology/Hematology (2022): 103725.

Peck, Tabitha C., et al. "Effects of Transparency on Perceived Humanness: Implications for Rendering Skin Tones Using Optical See-Through Displays." IEEE Transactions on Visualization and Computer Graphics 28.5 (2022): 2179-2189.

Hameed, Ahmad, et al. "Skin lesion classification in dermoscopic images using stacked Convolutional Neural Network." Journal of Ambient Intelligence and Humanized Computing (2021): 1-15.

Adla, Devakishan, et al. "Deep learning-based computer-aided diagnosis model for skin cancer detection and classification." Distributed and Parallel Databases 40.4 (2022): 717-736.

Liu, Yu Han. "Feature extraction and image recognition with convolutional neural networks." Journal of Physics: Conference Series. Vol. 1087. No. 6. IOP Publishing, 2018.

Wu, Jiangpeng, et al. "Rapid and accurate identification of COVID-19 infection through machine learning based on clinically available blood test results." MedRxiv (2020).

Ragab, Mahmoud, et al. "Early and accurate melanoma skin cancer detection using the hybrid level set approach." Frontiers in Physiology 13 (2022): 2536.

Melbin, K., and Y. Jacob Vetha Raj. "An enhanced model for skin disease detection using dragonfly optimization based deep neural network." 2019 Third International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC). IEEE, 2019.

Verma, Anurag Kumar, and Saurabh Pal. "Prediction of skin disease with three different feature selection techniques using stacking ensemble method." Applied biochemistry and biotechnology 191.2 (2020): 637-656.

Khan, M. Attique, et al. "An integrated skin lesion detection and recognition framework through saliency method and optimal deep neural network features selection." Neural Computing and Applications 32.20 (2020): 15929-15948.

Srinivasu, Parvathaneni Naga, et al. "Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM." Sensors 21.8 (2021): 2852.

Goceri, Evgin."Diagnosis of skin diseases in deep learning and mobile technology." Computers in Biology and Medicine 134 (2021): 104458.

Xiong, Xin, XuexunGuo, and Yiping Wang. "Modeling of Human Skin by the Use of Deep Learning." Complexity 2021 (2021).

Khan, Muhammad Attique, et al. "Skin lesion segmentation and multiclass classification using deep learning features and improved moth flame optimization." Diagnostics 11.5 (2021): 811.

Lee, Kyungsu, et al. "Multi-Task and Few-Shot Learning-Based Fully Automatic Deep Learning Platform for Mobile Diagnosis of Skin Diseases." IEEE Journal of Biomedical and Health Informatics (2022).

Adla, Devakishan, et al. "Deep learning-based computer-aided diagnosis model for skin cancer detection and classification." Distributed and Parallel Databases 40.4 (2022): 717-736.

Afza, Farhat, et al. "A hierarchical three-step superpixels and deep learning framework for skin lesion classification." Methods 202 (2022): 88-102.

Bissoto, Alceu, et al. "(De) Constructing bias on skin lesion datasets." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2019.

Ancuti, Codruta O., et al. "Color channel compensation (3C): A fundamental preprocessing step for image enhancement." IEEE Transactions on Image Processing 29 (2019): 2653-2665.

Huether, Brian M., Steven C. Gustafson, and Randy P. Broussard."Wavelet preprocessing for high range resolution radar classification." IEEE Transactions on Aerospace and Electronic Systems 37.4 (2001): 1321-1332.

Ramamoorthy, M., et al. "Earlier Detection of Brain Tumor by Pre-Processing Based on Histogram Equalization with Neural Network." Healthcare.Vol. 10.No. 7.MDPI, 2022.

Huang, Mengxing, Wenjiao Yu, and Donghai Zhu. "An improved image segmentation algorithm based on the Otsu method." 2012 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing.IEEE, 2012.

Garg, Meenakshi, and Gaurav Dhiman. "A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants." Neural Computing and Applications 33.4 (2021): 1311-1328.

Chen, Xuan, et al. "An improved Wolf pack algorithm for optimization problems: Design and evaluation." Plos one 16.8 (2021): e0254239.

Basha, SH Shabbeer, et al. "Impact of fully connected layers on the performance of convolutional neural networks for image classification."Neurocomputing 378 (2020): 112-119.

Ngugi, Lawrence C., MoatazAbelwahab, and Mohammed Abo-Zahhad. "Recent advances in image processing techniques for automated leaf pest and disease recognition–A review." Information processing in agriculture 8.1 (2021): 27-51.

Lorenzo, Daniel, et al. "Prognostic factors and decision tree for long-term survival in metastatic uveal melanoma." Cancer research and treatment: official journal of Korean Cancer Association 50.4 (2018): 1130-1139.

Priya, R. Devi, et al. "Tri-staged feature selection in multi-class heterogeneous datasets using memetic algorithm and cuckoo search optimization." Expert Systems with Applications 209 (2022): 118286.

Refianti, Rina, Achmad Benny Mutiara, and RachmadinnaPoetriPriyandini."Classification of melanoma skin cancer using convolutional neural network."International Journal of Advanced Computer Science and Applications 10.3 (2019).

Downloads

Published

27.12.2023

How to Cite

Rajeswari, R. ., Kalaiselvi, K. ., Jayashri, N. ., Lakshmi, P. ., & Muthusamy, A. . (2023). Meta-Heuristic Based Melanoma Skin Disease Detection and Classification Using Wolf Antlion Neural Network (WALNN) Model. International Journal of Intelligent Systems and Applications in Engineering, 12(9s), 87–95. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4207

Issue

Vol. 12 No. 9s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.