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DocKG: A Knowledge Graph Framework for Health with Doctor-in-the-Loop Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Classification of Skin Pigmented Lesions Based on Deep Residual Network

Authors : Yunfei Qi, Shaofu Lin, Zhisheng Huang

Published in: Health Information Science

Publisher: Springer International Publishing

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Abstract

There are various of skin pigmented lesions with high risk. Melanoma is one of the most dangerous forms of skin cancer. It is one of the important research directions of medical artificial intelligence to carry out classification research of skin pigmented lesions based on deep learning. It can assist doctors to make clinical diagnosis and make patients receive treatment as soon as possible to improve survival rate. Aiming at the similar and imbalanced dermoscopic image data of pigmented lesions, this paper proposes a deep residual network improved by Squeeze-and-Excitation module, and dynamic update class-weight, in batches, with model ensemble adjustment strategies to change the attention of imbalanced data. The results show that the above method can increase the average precision by 9.1%, the average recall by 15.3%, and the average F1-score by 12.2%, compared with the multi-class classification using the deep residual network. Thus, the above method is a better classification model and weight adjustment strategy.

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Metadata
Title
Classification of Skin Pigmented Lesions Based on Deep Residual Network
Authors
Yunfei Qi
Shaofu Lin
Zhisheng Huang
Copyright Year
2019
Book
Health Information Science

Print ISBN: 978-3-030-32961-7

Electronic ISBN: 978-3-030-32962-4

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-32962-4_6

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