Performance Comparison of Deep Learning-Based Classification of Skin Cancer

D. Gowthami, C. Gowri Shankar, K. Kathiravan, K. Dhanush, K. M. Dharshni, C. Lavanya

2025

Abstract

Skin cancer is among the most prevalent and perilous diseases globally, necessitating accurate and efficient categorisation methods for early detection. Deep learning methodologies have demonstrated significant potential in the automated identification of skin cancer by enhancing diagnostic precision and minimising human error. The main motive of this work is Deep Learning (DL)-based skin cancer classification, this work proposed NASNet DL model, compared to conventional Convolutional Neural Networks (CNNs). NASNet is a neural architecture designed through automated architecture search that optimizes feature extraction and classification accuracy while maintaining computational efficiency. Experimental results verify that NASNet surpasses most traditional CNNs in classification precision, recall, and F1-score. Consequently, the supremacy of NASNet will pave its way for usage in real applications in the domain of medicine that will eventually translate into early, improved patient diagnoses.

Paper Citation

in Harvard Style

Gowthami D., Shankar C., Kathiravan K., Dhanush K., Dharshni K. and Lavanya C. (2025). Performance Comparison of Deep Learning-Based Classification of Skin Cancer. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 146-152. DOI: 10.5220/0013909400004919

in Bibtex Style

@conference{icrdicct`2525,
author={D. Gowthami and C. Shankar and K. Kathiravan and K. Dhanush and K. Dharshni and C. Lavanya},
title={Performance Comparison of Deep Learning-Based Classification of Skin Cancer},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={146-152},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013909400004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Performance Comparison of Deep Learning-Based Classification of Skin Cancer
SN - 978-989-758-777-1
AU - Gowthami D.
AU - Shankar C.
AU - Kathiravan K.
AU - Dhanush K.
AU - Dharshni K.
AU - Lavanya C.
PY - 2025
SP - 146
EP - 152
DO - 10.5220/0013909400004919
PB - SciTePress