作者
Joanna Jaworek-Korjakowska, Andrzej Brodzicki, Bill Cassidy, Connah Kendrick, Moi Hoon Yap
发表日期
2021/12/1
期刊
Cancers
卷号
13
期号
23
页码范围
6048
出版商
MDPI
简介
Simple Summary
The detection of skin moles driven by current deep learning based approaches yields impressive results in the classification of malignant melanoma. It has been observed that the specific criteria for in situ and early invasive melanoma highly depend on the anatomic site of the body. To address this problem, we propose a deep learning architecture based framework to classify skin lesions into the three most important anatomic sites, including the face, trunk and extremities, and acral lesions. In this study, we take advantage of pretrained networks,we perform in depth analysis on database, architecture, and result regarding the effectiveness of the proposed framework. Experiments confirm the ability of the developed algorithms to classify skin lesions into the most important anatomical sites with 91.45% overall accuracy for the EfficientNetB0 architecture, which is a state-of-the-art result in this domain.
Abstract
Over the past few decades, different clinical diagnostic algorithms have been proposed to diagnose malignant melanoma in its early stages. Furthermore, the detection of skin moles driven by current deep learning based approaches yields impressive results in the classification of malignant melanoma. However, in all these approaches, the researchers do not take into account the origin of the skin lesion. It has been observed that the specific criteria for in situ and early invasive melanoma highly depend on the anatomic site of the body. To address this problem, we propose a deep learning architecture based framework to classify skin lesions into the three most important anatomic sites …
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J Jaworek-Korjakowska, A Brodzicki, B Cassidy… - Cancers, 2021