TY - GEN
T1 - Exploring Deep Learning Techniques in Skin Lesion Detection
T2 - 5th International Conference on Data Analytics and Management, ICDAM 2024
AU - Vinod, Vidhu
AU - Pathan, Sameena
AU - Soman, Anetha Mary
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Skin cancer presents a significant global health concern, with its incidence steadily increasing over recent decades. This disease affects millions worldwide, presenting considerable challenges to healthcare systems and societies. Skin lesions serve as crucial markers for diagnosing and monitoring skin cancer, revealing abnormal cell growth visibly. Given the escalating prevalence of skin cancer and the imperative for early detection, there is a pressing need for computer-aided tools in skin lesion detection. Researchers have utilized artificial intelligence (AI) and deep learning to advance lesion detection, addressing this urgent requirement. Amid the rising prevalence of skin cancer, this paper delves into various deep learning methodologies, including convolutional neural networks (CNNs) and computer vision techniques, which have shown promising outcomes in automating skin lesion detection comparable to expert dermatologists.
AB - Skin cancer presents a significant global health concern, with its incidence steadily increasing over recent decades. This disease affects millions worldwide, presenting considerable challenges to healthcare systems and societies. Skin lesions serve as crucial markers for diagnosing and monitoring skin cancer, revealing abnormal cell growth visibly. Given the escalating prevalence of skin cancer and the imperative for early detection, there is a pressing need for computer-aided tools in skin lesion detection. Researchers have utilized artificial intelligence (AI) and deep learning to advance lesion detection, addressing this urgent requirement. Amid the rising prevalence of skin cancer, this paper delves into various deep learning methodologies, including convolutional neural networks (CNNs) and computer vision techniques, which have shown promising outcomes in automating skin lesion detection comparable to expert dermatologists.
UR - https://www.scopus.com/pages/publications/105013046427
UR - https://www.scopus.com/pages/publications/105013046427#tab=citedBy
U2 - 10.1007/978-981-96-3355-5_40
DO - 10.1007/978-981-96-3355-5_40
M3 - Conference contribution
AN - SCOPUS:105013046427
SN - 9789819633548
T3 - Lecture Notes in Networks and Systems
SP - 529
EP - 537
BT - Proceedings of Data Analytics and Management, ICDAM 2024
A2 - Swaroop, Abhishek
A2 - Virdee, Bal
A2 - Correia, Sérgio Duarte
A2 - Polkowski, Zdzislaw
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 14 June 2024 through 15 June 2024
ER -