A deep learning, image based approach for automated diagnosis for inflammatory skin diseases.
Ontology highlight
ABSTRACT: Background
As the booming of deep learning era, especially the advances in convolutional neural networks (CNNs), CNNs have been applied in medicine fields like radiology and pathology. However, the application of CNNs in dermatology, which is also based on images, is very limited. Inflammatory skin diseases, such as psoriasis (Pso), eczema (Ecz), and atopic dermatitis (AD), are very easily to be mis-diagnosed in practice.Methods
Based on the EfficientNet-b4 CNN algorithm, we developed an artificial intelligence dermatology diagnosis assistant (AIDDA) for Pso, Ecz & AD and healthy skins (HC). The proposed CNN model was trained based on 4,740 clinical images, and the performance was evaluated on experts-confirmed clinical images grouped into 3 different dermatologist-labelled diagnosis classifications (HC, Pso, Ecz & AD).Results
The overall diagnosis accuracy of AIDDA is 95.80%±0.09%, with the sensitivity of 94.40%±0.12% and specificity 97.20%±0.06%. AIDDA showed accuracy for Pso is 89.46%, with sensitivity of 91.4% and specificity of 95.48%, and accuracy for AD & Ecz 92.57%, with sensitivity of 94.56% and specificity of 94.41%.Conclusions
AIDDA is thus already achieving an impact in the diagnosis of inflammatory skin diseases, highlighting how deep learning network tools can help advance clinical practice.
SUBMITTER: Wu H
PROVIDER: S-EPMC7290553 | biostudies-literature | 2020 May
REPOSITORIES: biostudies-literature
Publications
A deep learning, image based approach for automated diagnosis for inflammatory skin diseases.
Wu Haijing H Yin Heng H Chen Haipeng H Sun Moyuan M Liu Xiaoqing X Yu Yizhou Y Tang Yang Y Long Hai H Zhang Bo B Zhang Jing J Zhou Ying Y Li Yaping Y Zhang Guiyuing G Zhang Peng P Zhan Yi Y Liao Jieyue J Luo Shuaihantian S Xiao Rong R Su Yuwen Y Zhao Juanjuan J Wang Fei F Zhang Jing J Zhang Wei W Zhang Jin J Lu Qianjin Q
Annals of translational medicine 20200501 9
<h4>Background</h4>As the booming of deep learning era, especially the advances in convolutional neural networks (CNNs), CNNs have been applied in medicine fields like radiology and pathology. However, the application of CNNs in dermatology, which is also based on images, is very limited. Inflammatory skin diseases, such as psoriasis (Pso), eczema (Ecz), and atopic dermatitis (AD), are very easily to be mis-diagnosed in practice.<h4>Methods</h4>Based on the EfficientNet-b4 CNN algorithm, we deve ...[more]
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