Early Diagnosis Model of Mycosis Fungoides Based on Intelligent Analysis of Dermoscopic Images

LIU Zhaorui; ZHANG Yilan; XIE Fengying; LIU Jie

doi:10.12290/xhyxzz.2021-0496

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Nov 2021

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JOURNAL OF MECHANICAL ENGINEERING > 2021 > 12(5): 689-697.

LIU Zhaorui, ZHANG Yilan, XIE Fengying, LIU Jie. Early Diagnosis Model of Mycosis Fungoides Based on Intelligent Analysis of Dermoscopic Images[J]. JOURNAL OF MECHANICAL ENGINEERING, 2021, 12(5): 689-697. doi: 10.12290/xhyxzz.2021-0496

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LIU Zhaorui, ZHANG Yilan, XIE Fengying, LIU Jie. Early Diagnosis Model of Mycosis Fungoides Based on Intelligent Analysis of Dermoscopic Images[J]. JOURNAL OF MECHANICAL ENGINEERING, 2021, 12(5): 689-697. doi: 10.12290/xhyxzz.2021-0496

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Early Diagnosis Model of Mycosis Fungoides Based on Intelligent Analysis of Dermoscopic Images

doi: 10.12290/xhyxzz.2021-0496

LIU Zhaorui¹,
ZHANG Yilan²,
XIE Fengying^{2
,
,},
LIU Jie^{1
,
,}

Funds:

National Natural Science Foundation of China 61871011

National Natural Science Foundation of China 62071011

National Natural Science Foundation of China 61771031

National Natural Science Foundation of China 82173449

The Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences 2019XK320024

Beijing Natural Science Foudation 4192032

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Corresponding author: XIE Fengying Tel: 86-10-61716929, E-mail: xfy_73@buaa.edu.cn; LIU Jie Tel: 86-10-69151536, E-mail: Liujie04672@pumch.cn
Received Date: 26 Jun 2021
Accepted Date: 02 Aug 2021

Available Online: 26 Nov 2021

Issue Publish Date: 30 Sep 2021

Abstract

Abstract

Objective To compare the application value of the binary classification model based on dermoscopic images of convolutional neural network (CNN) in the diagnosis of mycosis fungoides (MF) and inflammatory dermatosis.
Methods Patients diagnosed with early MF or inflammatory dermatosis with similar clinical manifestations in the dermatology clinic of Peking Union Medical College Hospital from January 2016 to December 2020 were retrospectively included. The patients were divided into the training set and the test set at a ratio of 4∶1. Six classical network structures were trained by using the dermoscopic images of patients in the training set, and the CNN binary classification model was constructed by using transfer learning. At the same time, in the test set, 1 image of each patient that was randomly selected, together with clinical images of the skin lesions, was interpreted by 13 dermatologists. Compare the CNN binary classification model with dermatologists in the differential diagnosis of early MF and inflammatory dermatosis in the test set. The results were expressed in terms of area under the curve (AUC), sensitivity, specificity, Kappa coefficient, etc., and receiver operating characteristic (ROC) curve was used for visual analysis.
Results A total of 48 patients with early MF (402 dermoscopic images) and 96 patients with inflammatory dermatosis (557 dermoscopic images) were included. Among them, there were 117 cases in the training set (772 dermoscopic images), and 27 cases in the test set (187 dermoscopic images). In the test set, the sensitivity and specificity of dermatologists in the differential diagnosis of early MF and inflammatory dermatosis were 70.19% (95% CI: 59.68%-80.70%) and 94.74% (95% CI: 91.77%-97.71%) respectively, and the Kappa coefficient is 0.677(95% CI: 0.566-0.789). When classified by the single image, the AUC of the CNN binary classification model for the differential diagnosis of early MF and inflammatory dermatosis was 0.87 (95% CI: 0.84-0.89); the sensitivity and specificity were 75.02% (95% CI: 70.19%-79.85%) and 82.02% (95% CI: 79.30%-84.87%), respectively; the Kappa coefficient was 0.563(95% CI: 0.507-0.620). When classified by cases, the AUC of the CNN binary classification model for the differential diagnosis of early MF and inflammatory dermatosis was 0.97 (95% CI: 0.95-0.99); the sensitivity and specificity were 87.50% (95% CI: 78.55%-96.45%) and 93.85% (95% CI: 88.93%-98.77%), respectively; the Kappa coefficient was 0.920(95% CI: 0.884-0.954). The ROC curve showed that the AUC of the CNN binary classification model with EfficientNet-B0 for diagnosing MF was 0.99 when classified by cases, the sensitivity and specifity were 88.9% and 100%, and the corresponding point of the average diagnostic sensitivity and specificity of 13 dermatologists were at the lower right of the curve.
Conclusions The CNN binary classification model based on the intelligent analysis of dermoscopic images can accurately classify early MF and inflammatory dermatosis, and its ability of differential diagnosis is better than the average level of dermatologists.
- early mycosis fungoides,
- inflammatory dermatosis,
- dermoscopic image,
- convolutional neural network

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References

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Early Diagnosis Model of Mycosis Fungoides Based on Intelligent Analysis of Dermoscopic Images

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Early Diagnosis Model of Mycosis Fungoides Based on Intelligent Analysis of Dermoscopic Images

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