A Review on Skin Melanoma Classification using different ML and DL Models

Viranchkumar Mayurbhai Kadia; Dr. Sheshang Degadwala

doi:10.32628/CSEIT24103124

Authors

Viranchkumar Mayurbhai Kadia Reserach Scholar, Department of Computer Engineering, Sigma University, Vadodara, Gujarat, India Author
Dr. Sheshang Degadwala Professor / HOD, Department of Computer Engineering, Sigma University, Vadodara, Gujarat, India Author

DOI:

https://doi.org/10.32628/CSEIT24103124

Keywords:

Skin Melanoma, Machine Learning, Deep Learning, Classification Models, Diagnostic Accuracy

Abstract

This review explores the classification of skin melanoma utilizing various machine learning (ML) and deep learning (DL) models, highlighting the advancements and comparative performance of these methodologies. Skin melanoma, a serious type of skin cancer, demands early and accurate diagnosis for effective treatment. The review covers a range of ML techniques such as support vector machines, decision trees, and ensemble methods, alongside \DL approaches including convolutional neural networks and recurrent neural networks. Emphasis is placed on the models' accuracy, computational efficiency, and the datasets used for training and validation. The review underscores the potential of DL models to outperform traditional ML methods due to their ability to automatically extract and learn intricate features from large datasets, thus offering promising prospects for enhanced diagnostic precision in melanoma classification.

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References

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D. D. Pandya, N. S. Gupta, A. Jadeja, R. D. Patel, S. Degadwala, and D. Vyas, “Bias Protected Attributes Data Balancing using Map Reduce,” in 6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022 - Proceedings, 2022, pp. 1540–1544. doi: 10.1109/ICECA55336.2022.10009363. DOI: https://doi.org/10.1109/ICECA55336.2022.10009363

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A. Patel, S. Degadwala, and D. Vyas, “Lung Respiratory Audio Prediction using Transfer Learning Models,” in 6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022 - Proceedings, 2022, pp. 1107–1114. doi: 10.1109/I-SMAC55078.2022.9986498. DOI: https://doi.org/10.1109/I-SMAC55078.2022.9986498

M. Shah, S. Degadwala, and D. Vyas, “Diet Recommendation System based on Different Machine Learners: A Review,” in Proceedings of the 2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022, 2022, pp. 290–295. doi: 10.1109/ICAIS53314.2022.9742919. DOI: https://doi.org/10.1109/ICAIS53314.2022.9742919

H. Gupta, D. Patel, A. Makade, K. Gupta, O. P. Vyas, and A. Puliafito, “Risk Prediction in the Life Insurance Industry Using Federated Learning Approach,” in MELECON 2022 - IEEE Mediterranean Electrotechnical Conference, Proceedings, 2022, pp. 948–953. doi: 10.1109/MELECON53508.2022.9842869. DOI: https://doi.org/10.1109/MELECON53508.2022.9842869

D. D. Pandya, A. Jadeja, S. Degadwala, and D. Vyas, “Ensemble Learning based Enzyme Family Classification using n-gram Feature,” in Proceedings - 2022 6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022, 2022, pp. 1386–1392. doi: 10.1109/ICICCS53718.2022.9788292. DOI: https://doi.org/10.1109/ICICCS53718.2022.9788292

B. Trivedi, S. Degadwala, and D. Vyas, “Parallel Data Stream Anonymization Methods: A Review,” in Proceedings of the 2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022, 2022, pp. 887–891. doi: 10.1109/ICAIS53314.2022.9743084. DOI: https://doi.org/10.1109/ICAIS53314.2022.9743084

P. Bam, S. Degadwala, R. Upadhyay, and D. Vyas, “Spoken Language Recognization Based on Features and Classification Methods: A Review,” in Proceedings of the 2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022, 2022, pp. 868–873. doi: 10.1109/ICAIS53314.2022.9743090. DOI: https://doi.org/10.1109/ICAIS53314.2022.9743090

R. Baria, S. Degadwala, R. Upadhyay, and D. Vyas, “Theoretical Evaluation of Machine And Deep Learning For Detecting Fake News,” in Proceedings of the 2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022, 2022, pp. 325–329. doi: 10.1109/ICAIS53314.2022.9742864. DOI: https://doi.org/10.1109/ICAIS53314.2022.9742864

S. Degadwala, D. Vyas, U. Chakraborty, A. R. Dider, and H. Biswas, “Yolo-v4 Deep Learning Model for Medical Face Mask Detection,” in Proceedings - International Conference on Artificial Intelligence and Smart Systems, ICAIS 2021, 2021, pp. 209–213. doi: 10.1109/ICAIS50930.2021.9395857. DOI: https://doi.org/10.1109/ICAIS50930.2021.9395857

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S. Degadwala, D. Vyas, and H. Dave, “Classification of COVID-19 cases using Fine-Tune Convolution Neural Network (FT-CNN),” in Proceedings - International Conference on Artificial Intelligence and Smart Systems, ICAIS 2021, 2021, pp. 609–613. doi: 10.1109/ICAIS50930.2021.9395864. DOI: https://doi.org/10.1109/ICAIS50930.2021.9395864

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