Ding Yuhan, Yi Zhenglin, Li Mengjuan, Long Jianhong, Lei Shaorong, Guo Yu, Fan Pengju, Zuo Chenchen, Wang Yongjie
Department of Burns and Plastic Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.
National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
Digit Health. 2023 Oct 12;9:20552076231207197. doi: 10.1177/20552076231207197. eCollection 2023 Jan-Dec.
To develop an explainable lightweight skin disease high-precision classification model that can be deployed to the mobile terminal.
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.
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 PH dataset, an excellent performance score is obtained, which shows that it has good generalization performance.
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.
开发一种可解释的轻量级皮肤疾病高精度分类模型，并能够部署到移动终端。
在本研究中，我们提出了HI-MViT，一种基于改进的MobileViT的用于可解释皮肤疾病分类的轻量级网络。HI-MViT主要由普通卷积、改进的MV2、MobileViT模块、全局池化和全连接层组成。改进的MV2使用捷径和深度可分类卷积的组合，在确保信息交互和存储高效实现的同时，大幅减少计算量。MobileViT模块能够有效地编码局部和全局信息。此外，还使用语义特征降维可视化和类激活映射可视化方法对HI-MViT进行研究，以进一步了解模型在学习皮肤病变图像时的注意力区域。
国际皮肤成像协作组织已经收集并提供了ISIC系列数据集。在ISIC-2018数据集上使用HI-MViT模型进行实验，在F1分数、准确率、平均精度（AP）和曲线下面积（AUC）方面分别取得了0.931、0.932、0.961和0.977的分数。与ISIC-2018任务3的前五种算法相比，Marco的平均F1分数、AP和AUC与次优性能模型相比分别提高了6.9%、6.8%和0.8%。与最具竞争力的卷积神经网络架构ConvNeXt相比，我们的模型在F1分数、准确率、AP和AUC方面分别高出5.0%、3.4%、2.3%和2.2%。在ISIC-2017数据集上的实验也取得了优异的结果，所有指标均优于ISIC-2017任务3的前五种算法。使用训练好的模型在PH数据集上进行测试，获得了优异的性能分数，这表明它具有良好的泛化性能。
本文提出的皮肤疾病分类模型HI-MViT在实验中表现出优异的分类性能和泛化性能。它展示了分类结果如何应用于皮肤科医生的计算机辅助诊断，使医学专业人员能够更快速、可靠地对各种皮肤镜图像进行分类。
