Systematic Investigation on Deep Learning Network in Skin Cancer Diagnosis

Sihan Bian

2024

Abstract

Skin cancer is raising global concern in healthcare. Researchers are looking into the application of deep learning networks in skin cancer diagnosis, which is full of potential in saving labour and time. This paper summarizes the framework of machine learning algorithms in skin cancer detection, and reviews several recent studies on deep learning of skin cancer diagnosis. The approaches from these studies fall into three primary categories: classification, segmentation, and the creation of supplementary data. Techniques like Grad-CAM are integrated with Explainable Artificial Intelligence for the classification of skin lesions, offering insights by emphasizing critical regions. Additionally, the paper touches on the constraints and hurdles associated with employing deep learning for diagnosing skin cancer, noting common problems such as a lack of data diversity and concerns over privacy protection. The influence of parameters on model efficacy and the limited scope of interpretable models to explanations based on individual samples are highlighted. Furthermore, it's pointed out that deep learning models have not been sufficiently tested in clinical settings. In conclusion, the paper summarizes the methods evaluated and underscores that deep learning frameworks require further exploration and enhancements before they can be reliably used in clinical settings without direct oversight from medical professionals.

Paper Citation

in Harvard Style

Bian S. (2024). Systematic Investigation on Deep Learning Network in Skin Cancer Diagnosis. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 469-473. DOI: 10.5220/0012953200004508

in Bibtex Style

@conference{emiti24,
author={Sihan Bian},
title={Systematic Investigation on Deep Learning Network in Skin Cancer Diagnosis},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={469-473},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012953200004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Systematic Investigation on Deep Learning Network in Skin Cancer Diagnosis
SN - 978-989-758-713-9
AU - Bian S.
PY - 2024
SP - 469
EP - 473
DO - 10.5220/0012953200004508
PB - SciTePress