HI-MViT: A lightweight model for explainable skin disease classification based on modified MobileViT

Author:

Ding Yuhan123,Yi Zhenglin24,Li Mengjuan12,long Jianhong12,Lei Shaorong12,Guo Yu12,Fan Pengju12,Zuo Chenchen12,Wang Yongjie12ORCID

Affiliation:

1. Department of Burns and Plastic Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China

2. National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China

3. School of Computer Science and Engineering, Central South University, Changsha, Hunan, China

4. Departments of Urology, Xiangya Hospital, Central South University, Changsha, China

Abstract

Objective To develop an explainable lightweight skin disease high-precision classification model that can be deployed to the mobile terminal. Methods In this study, we present HI-MViT, a lightweight network for explainable skin disease classification based on Modified MobileViT. HI-MViT is mainly composed of ordinary convolution, Improved-MV2, MobileViT block, global pooling, and fully connected layers. Improved-MV2 uses the combination of shortcut and depth classifiable convolution to substantially decrease the amount of computation while ensuring the efficient implementation of information interaction and memory. The MobileViT block can efficiently encode local and global information. In addition, semantic feature dimensionality reduction visualization and class activation mapping visualization methods are used for HI-MViT to further understand the attention area of the model when learning skin lesion images. Results The International Skin Imaging Collaboration has assembled and made available the ISIC series dataset. Experiments using the HI-MViT model on the ISIC-2018 dataset achieved scores of 0.931, 0.932, 0.961, and 0.977 on F1-Score, Accuracy, Average Precision (AP), and area under the curve (AUC). Compared with the top five algorithms of ISIC-2018 Task 3, Marco's average F1-Score, AP, and AUC have increased by 6.9%, 6.8%, and 0.8% compared with the suboptimal performance model. Compared with ConvNeXt, the most competitive convolutional neural network architecture, our model is 5.0%, 3.4%, 2.3%, and 2.2% higher in F1-Score, Accuracy, AP, and AUC, respectively. The experiments on the ISIC-2017 dataset also achieved excellent results, and all indicators were better than the top five algorithms of ISIC-2017 Task 3. Using the trained model to test on the PH2 dataset, an excellent performance score is obtained, which shows that it has good generalization performance. Conclusions The skin disease classification model HI-MViT proposed in this article shows excellent classification performance and generalization performance in experiments. It demonstrates how the classification outcomes can be applied to dermatologists’ computer-assisted diagnostics, enabling medical professionals to classify various dermoscopic images more rapidly and reliably.

Funder

Department of Science and Technology of Hunan Province

Publisher

SAGE Publications

Subject

Health Information Management,Computer Science Applications,Health Informatics,Health Policy

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