Skin Cancer Detection: Using Deep Learning and Transfer Learning Techniques

DOI

10.2991/978-94-6463-496-9_20How to use a DOI?

Keywords

Deep Learning; Transfer Learning; VGG16; VGG19; Res-net50; Melanoma; Skin Lesion

Abstract

Skin cancer is one of the most perilous forms of cancer, stemming from unrepaired DNA damage in skin cells, leading to genetic abnormalities or mutations. Its tendency to slowly spread to other body parts underscores the critical importance of early detection. Researchers have thus devised various early detection methods, utilizing parameters such as symmetry, color, size, and shape of lesions. An innovative approach employing deep learning and transfer learning has emerged, achieving up to a 95% correct classification rate of malignant lesions from skin images. This breakthrough offers hope in the fight against melanoma by enabling earlier and more precise diagnoses, crucial for swift treatment. However, the scarcity of skilled dermatologists globally remains a challenge in addressing current healthcare needs. This article sheds light on the challenges and clinical testimonies surrounding this major advancement in skin cancer treatment, illustrating both the benefits and hurdles of integrating AI techniques in dermatology and medicine.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Rami Djekoun
AU  - Nadir Farah
PY  - 2024
DA  - 2024/08/31
TI  - Skin Cancer Detection: Using Deep Learning and Transfer Learning Techniques
BT  - Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)
PB  - Atlantis Press
SP  - 261
EP  - 269
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-496-9_20
DO  - 10.2991/978-94-6463-496-9_20
ID  - Djekoun2024
ER  -