Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of Data Analytics and Management, ICDAM 2024 |
| Editors | Abhishek Swaroop, Bal Virdee, Sérgio Duarte Correia, Zdzislaw Polkowski |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 529-537 |
| Number of pages | 9 |
| ISBN (Print) | 9789819633548 |
| DOIs | |
| Publication status | Published - 2025 |
| Event | 5th International Conference on Data Analytics and Management, ICDAM 2024 - London, United Kingdom Duration: 14-06-2024 → 15-06-2024 |
Publication series
| Name | Lecture Notes in Networks and Systems |
|---|---|
| Volume | 1298 LNNS |
| ISSN (Print) | 2367-3370 |
| ISSN (Electronic) | 2367-3389 |
Conference
| Conference | 5th International Conference on Data Analytics and Management, ICDAM 2024 |
|---|---|
| Country/Territory | United Kingdom |
| City | London |
| Period | 14-06-24 → 15-06-24 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Signal Processing
- Computer Networks and Communications
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