Combined deep learning and machine learning models for the prediction of stages of melanoma
Abstract
Melanoma is deadly kind of skin cancer as it gets metastasized soon. It is really essential to recognize melanoma and start treatment at early stage. It is also necessary to determine the stage of melanoma in order to treat melanoma patients. Non-invasive technique is required to detect the stage of melanoma. Proposed system presents novel technique to classify the stages of melanoma based on thickness of tumor. This system uses dimensionality reduction technique to reduce the number of features and it also uses combine approach of deep learning (DL) and machine learning (ML) algorithm which include multilayer perception (MLP) and random forest (RF). Deep learning method is always better for training as they can reduce the need for data preprocessing and feature engineering and can provide simple trainable models built using only five or six different operations. Secondly, they are scalable, as they can be easily parallelized on GPUs or TPUs and can be trained by iterating over small batches of data. Thirdly they are reusable, so they can be trained on additional data without starting from scratch, making them viable for continuous online learning. For classification task machine learning algorithm that is, random forest is used as it decreases over fitting in decision trees and aids to increase the accuracy. Total of three algorithms were used, MLP, RF and proposed algorithm combined multilayer perception and random forest that is, MLP-RF. Among these models, the MLP-RF showed the best results in predicting melanoma stages with the accuracy of 97.42%.
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1. Skin cancer 101. Available online: http://www.skincancer.org/skin-cancer-information/ (accessed on 23 October 2023).
2. Melanoma overview. Available online: http://www.skincancer.org/skin-cancer-information/melanoma (accessed on 23 October 2023).
3. Vestergaard ME, Macaskill P, Holt PE, Menzies SW. Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: A meta-analysis of studies performed in a clinical setting. British Journal of Dermatology 2008; 159(3): 669–676. doi: 10.1111/j.1365-2133.2008.08713.x
4. Marine A, Walter B. Non-invasive determination of Breslow index. In: Cao MY (editor). Current Manage Malignant Melanoma. IntechOpen; 2011. pp. 29–44.
5. Breslow A. Thickness, cross-sectional areas and depth of invasion in the prognosis of cutaneous melanoma. Annals of Surgery 1970; 172(5): 902–908. doi: 10.1097/00000658-197011000-00017
6. Marghoob AA, Koenig K, Bittencourt FV, et al. Breslow thickness and clark level in melanoma: Support for including level in pathology reports and in America joint committee on cancer staging. Cancer 2000; 88(3): 589–595.
7. Patil R, Bellary S. Machine learning approach in melanoma cancer stage detection. Journal of King Saud University—Computer and Information Sciences 2022; 34(6): 3285–3293. doi: 10.1016/j.jksuci.2020.09.002
8. Saez A, Sanchez-Monedero J, Gutierrez PA, Hervas-Martinez C. Machine learning methods for binary and multiclass classification of melanoma thickness from dermoscopic images. IEEE Transactions on Medical Imaging 2016; 35(4): 1036–1045. doi: 10.1109/tmi.2015.2506270
9. Rubegni P, Cevenini G, Sbano P, et al. Evaluation of cutaneous melanoma thickness by digital dermoscopy analysis: A retrospective study. Melanoma Research 2010; 20(3): 212–217. doi: 10.1097/cmr.0b013e328335a8ff
10. Jaworek-Korjakowska J, Kleczek P, Gorgon M. Melanoma thickness prediction based on convolutional neural network with VGG-19 model transfer learning. In: Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 16–17 June 2019; Long Beach, CA, USA. pp. 2748–2756.
11. Naeem A, Farooq MS, Khelifi A, Abid A. Malignant melanoma classification using deep learning: Datasets, performance measurements, challenges and opportunities. IEEE Access 2020; 8: 110575–110597. doi: 10.1109/ACCESS.2020.3001507
12. Reshma M, Shan BP. Two methodologies for identification of stages and different types of melanoma detection. In: Proceedings of the 2017 Conference on Emerging Devices and Smart Systems (ICEDSS); 3–4 March 2017; Mallasamudram, India. pp. 257–259.
13. Patil R, Bellary S. Machine learning approach for malignant melanoma classification. International Journal of Science, Technology, Engineering and Management–A VTU Publication 2021; 3(1): 40–46.
14. Gaikwad M, Gaikwad P, Jagtap P, et al. Melanoma cancer detection using deep learning. International Journal of Scientific Research in Science, Engineering and Technology 2020; 7(3): 394–400. doi: 10.32628/IJSRET
15. Patil R, Bellary S. Transfer learning based system for melanoma type detection. Revue d’Intelligence Artificielle 2021; 35(2): 123–130. doi: 10.18280/ria.350203
16. Patil R, Mote A, Mane D. Detection of malignant melanoma using hybrid algorithm. In: Singh P, Singh D, Tiwari V, et al. (editors). Machine Learning and Computational Intelligence Techniques for Data Engineering, Proceedings of the MISP 2022: International Conference on Machine Intelligence and Signal Processing; 12–14 March 2022. Springer; 2023. Volume 998, pp. 773–782.
17. Ichim L, Popescu D. Melanoma detection using an objective system based on multiple connected neural networks. IEEE Access 2020; 8: 179189–179202. doi: 10.1109/ACCESS.2020.3028248
18. Albahli S, Nida N, Irtaza A, et al. Melanoma lesion detection and segmentation using YOLOv4-DarkNet and active contour. IEEE Access 2020; 8: 198403–198414. doi: 10.1109/ACCESS.2020.3035345
19. Zhang N, Cai YX, Wang YY, et al. Skin cancer diagnosis based on optimized convolutional neural network. Artificial Intelligence in Medicine 2020; 102: 101756. doi: 10.1016/j.artmed.2019.101756
20. Kassem MA, Hosny KM, Fouad MM. Skin lesions classification into eight classes for ISIC 2019 using deep convolutional neural network and transfer learning. IEEE Access 2020; 8: 114822–114832. doi: 10.1109/ACCESS.2020.3003890
21. Cirrincione G, Cannata S, Cicceri G, et al. Transformer-based approach to melanoma detection. Sensors 2023; 23(12): 5677. doi: 10.3390/s23125677
DOI: https://doi.org/10.32629/jai.v7i1.749
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Copyright (c) 2023 Rashmi Ashtagi, Deepak Mane, Mahendra Deore, Jyoti R. Maranur, Sridevi Hosmani
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