B Hema Kumari, Samudrala Shirisha*, Narmeta Aravind and Guram Akhil

Department of Information Technology, Sreenidhi Institute of Science and Technology, India

*Corresponding Author: Samudrala Shirisha, Department of Information Technology, Sreenidhi Institute of Science and Technology, India.

Received: May 02, 2024; Published: May 08, 2024

Abstract

Melanoma, which means to "black tumor," is the most risky kind of skin cancer. It has the ability to spread to any region of the body and grows quickly. Melanomas cannot be treated if they are not discovered in their early stages. Thus, early detection is crucial for melanoma treatment. A neural network will be used to identify melanoma cancer. Existing system use Neural Networks and Support Vector Machine to detect melanoma. The accuracy for Neural Network is 60%-75% and by using Support Vector Machine it is 80%. The main disadvantage of this model is no proper image preparation and the training takes a lot of time.

Keywords: Melanoma; Black Tumor; Skin Cancer

References

1. National Cancer Institute, PDQ Melanoma Treatment. Bethesda, MD, USA. (Nov. 4, 2019). PDQ Adult Treatment Editorial Board.
2. Cancer Statistics Center. American Cancer Society (2019).
3. Nabeel F Lattoofi., et al. “Melanoma Skin Cancer Detection Based on ABCD Rule”. First International Conference of Computer and Applied Sciences (CAS), (2019).
4. J A Curtin., et al. “Somatic activation of KIT in distinct subtypes of melanoma”. Journal of Clinical Oncology26 (2006): 4340–4346.
5. S Jain and N Pise. "Computer-aided melanoma skin cancer detection using image processing". Procedia Computer Science 48 (2015): 735-740.
6. ME Celebi., et al. “A methodological approach to the classification of dermoscopy images”. Computerized Medical Imaging and Graphics 6 (2007): 362-373.
7. Noel CF Codella., et al. “Skin Lesion Analysis Toward Melanoma Detection”. Hosted by the International Skin Imaging Collaboration (ISIC), (2017).
8. S Pratavieira., et al. “Optical imaging as auxiliary tool in skin cancer diagnosis”. in Skin Cancers-Risk Factors, Prevention Therapy, May 30 (2012): 159-173.
9. NE Marcon., et al. “Fluorescence and spectral imaging”. Scientific World Journal 7 (2007): 2046-2071.

Citation

Citation: Samudrala Shirisha., et al. “Classification and Detection of Melanoma Skin Cancer Using Deep Learning Models".Acta Scientific Computer Sciences 6.6 (2024): 10-14.

Copyright

Copyright: © 2024 Samudrala Shirisha., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.