A Review on Transfer Learning Approaches for Skin Melanoma Classification

Authors

  • Arti Pandey  Computer Engineering Department, Sigma Institute of Engineering, Vadodara, Gujarat, India
  • Dr. Sheshang Degadwala  Computer Engineering Department, Sigma Institute of Engineering, Vadodara, Gujarat, India
  • Dhairya Vyas  Managing Director, Shree Drashti Infotech LLP, Vadodara, Gujarat, India

DOI:

https://doi.org//10.32628/CSEIT228661

Keywords:

Skin Melanoma, Transfer Learning, AlexNet, VggNet, ResNet.

Abstract

Skin is important organ of our body which covers muscles, bones, and other parts of body. Melanoma is a kind of skin cancer that begins in melanocytes cell. It can influence on the skin only, or it may expand to the bones and organs. It is less common, but more serious and aggressive than other types of skin cancer. Majority of deaths related to skin cancer occur due to Melanoma over the world. For effective treatment it is very important to melanoma identified earlier as possible. As well as detection of the stages of melanoma to recognize depth of spreading of melanocyte cell in other organ of body. Process of Detection of Skin cancer is difficult, expensive, and time-consuming process. Purpose of this research review is to more accurate recognition the types of Melanomas and decrease ratio of false diagnosis using transfer learning model for melanoma classification using AlexNet, VggNet and ResNet. The working of the different transfer learning model, its pros. and cons. Are discuss in this paper.

References

  1. O. Sharif, M. M. Hoque, A. S. M. Kayes, R. Nowrozy, and I. H. Sarker, “Skin Cancer Disease Detection Using Transfer Learning Technique” sciences Learning Techniques,pp. 1–23, 2020.
  2. R. Patil and S. Bellary, “Machine learning approach in melanoma cancer stage detection,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 6, pp. 3285–3293, 2022, doi: 10.1016/j.jksuci.2020.09.002.
  3. M. A. Khan, K. Muhammad, M. Sharif, T. Akram, and V. H. C. D. Albuquerque, “Multi-Class Skin Lesion Detection and Classification via Teledermatology,” IEEE J. Biomed. Heal. Informatics, vol. 25, no. 12, pp. 4267–4275, 2021, doi: 10.1109/JBHI.2021.3067789.
  4. V. Anand, S. Gupta, A. Altameem, S. R. Nayak, R. C. Poonia, and A. K. J. Saudagar, “An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer,” Diagnostics, vol. 12, no. 7, 2022, doi: 10.3390/diagnostics12071628.
  5. K. Thurnhofer-Hemsi, E. Lopez-Rubio, E. Dominguez, and D. A. Elizondo, “Skin lesion classification by ensembles of deep convolutional networks and regularly spaced shifting,” IEEE Access, vol. 9, pp. 112193–112205, 2021, doi: 10.1109/ACCESS.2021.3103410.
  6. C. Zhao, R. Shuai, L. Ma, W. Liu, Di. Hu, and M. Wu, “Dermoscopy Image Classification Based on StyleGAN and DenseNet201,” IEEE Access, vol. 9, pp. 8659–8679, 2021, doi: 10.1109/ACCESS.2021.3049600.
  7. Y. Jusman, I. M. Firdiantika, D. A. Dharmawan, and K. Purwanto, “Performance of multi layer perceptron and deep neural networks in skin cancer classification,” LifeTech 2021 - 2021 IEEE 3rd Glob. Conf. Life Sci. Technol., no. LifeTech, pp. 534–538, 2021, doi: 10.1109/LifeTech52111.2021.9391876.
  8. M. A. Wahba and A. S. Ashour, “Automated Diagnosis of Skin Cancer for Healthcare: Highlights and Procedures,” Eng. Technol. Healthc., pp. 187–212, 2021, doi: 10.1002/9781119644316.ch10.
  9. C. K. Chin, D. A. Binti Awang Mat, and A. Y. Saleh, “Skin Cancer Classification using Convolutional Neural Network with Autoregressive Integrated Moving Average,” ACM Int. Conf. Proceeding Ser., pp. 18–22, 2021, doi: 10.1145/3467691.3467693.
  10. A. Javaid, M. Sadiq, and F. Akram, “Skin Cancer Classification Using Image Processing and Machine Learning,” Proc. 18th Int. Bhurban Conf. Appl. Sci. Technol. IBCAST 2021, pp. 439–444, 2021, doi: 10.1109/IBCAST51254.2021.9393198.
  11. R. L. Araújo, F. H. D. d. Araújo, and R. R. V. e. Silva, “Automatic segmentation of melanoma skin cancer using transfer learning and fine-tuning,” Multimed. Syst., vol. 28, no. 4, pp. 1239–1250, 2022, doi: 10.1007/s00530-021-00840-3.
  12. M. Frasca, M. Nappi, M. Risi, G. Tortora, and A. A. Citarella, “A comparison of neural network approaches for melanoma classification,” Proc. - Int. Conf. Pattern Recognit., pp. 2110–2117, 2020, doi: 10.1109/ICPR48806.2021.9412893.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2022-12-30

Issue

Volume 8, Issue 6, November-December-2022

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

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

How to Cite

[1]
Arti Pandey, Dr. Sheshang Degadwala, Dhairya Vyas, " A Review on Transfer Learning Approaches for Skin Melanoma Classification, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 6, pp.394-399, November-December-2022. Available at doi : https://doi.org/10.32628/CSEIT228661