Image Recognition of Pigmented Skin Diseases Based on Deep Learning

Xinyu Zhao

2024

Abstract

One of the most common skin conditions is pigmentary skin disease. It is also challenging to differentiate between the lesions of various pigmentary skin diseases with the unaided eye due to their striking similarity. The paper wishes to investigate whether deep learning image recognition can resolve this issue because deep learning technology has advanced significantly in recent years and has shown promise in a number of domains. In order to help the investigation, the paper modified the weights of three pigmented skin illnesses that have similar clinical features to help two deep learning models that paper used to identify to gain higher accuracy. The findings demonstrate that deep learning can effectively identify many forms of pigmented skin illnesses and is very helpful in the recognition of skin diseases. In subsequent research, the paper will attempt to use deep learning to determine the lesion's stage, which will be extremely beneficial for diagnosing pigmented skin conditions.

Paper Citation

in Harvard Style

Zhao X. (2024). Image Recognition of Pigmented Skin Diseases Based on Deep Learning. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 221-226. DOI: 10.5220/0013512900004619

in Bibtex Style

@conference{daml24,
author={Xinyu Zhao},
title={Image Recognition of Pigmented Skin Diseases Based on Deep Learning},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={221-226},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013512900004619},
isbn={978-989-758-754-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Image Recognition of Pigmented Skin Diseases Based on Deep Learning
SN - 978-989-758-754-2
AU - Zhao X.
PY - 2024
SP - 221
EP - 226
DO - 10.5220/0013512900004619
PB - SciTePress