[论文解读] Classification of Human Monkeypox Disease Using Deep Learning Models and Attention Mechanisms

Md Enamul Haque, Md. Rayhan Ahmed+2 more|arXiv (Cornell University)|Nov 21, 2022

Poxvirus research and outbreaks被引用 27

一句话总结

该研究将深度迁移学习模型与 CBAM 注意力结合用于基于图像的猴痘分类，识别 Xception-CBAM-Dense 为最佳架构，验证准确率达 83.89%。

ABSTRACT

As the world is still trying to rebuild from the destruction caused by the widespread reach of the COVID-19 virus, and the recent alarming surge of human monkeypox disease outbreaks in numerous countries threatens to become a new global pandemic too. Human monkeypox disease syndromes are quite similar to chickenpox, and measles classic symptoms, with very intricate differences such as skin blisters, which come in diverse forms. Various deep-learning methods have shown promising performances in the image-based diagnosis of COVID-19, tumor cell, and skin disease classification tasks. In this paper, we try to integrate deep transfer-learning-based methods, along with a convolutional block attention module (CBAM), to focus on the relevant portion of the feature maps to conduct an image-based classification of human monkeypox disease. We implement five deep-learning models, VGG19, Xception, DenseNet121, EfficientNetB3, and MobileNetV2, along with integrated channel and spatial attention mechanisms, and perform a comparative analysis among them. An architecture consisting of Xception-CBAM-Dense layers performed better than the other models at classifying human monkeypox and other diseases with a validation accuracy of 83.89%.

研究动机与目标

• 推动基于图像的人类猴痘诊断，并将其与类似综合征区分开来。
• 评估使用注意力机制的猴痘分类的迁移学习模型。
• 比较五种整合通道与空间注意力的模型以确定最佳架构。
• 评估在猴痘检测任务中 CBAM 增强网络的性能。

提出的方法

• 实现五种深度学习模型：VGG19、Xception、DenseNet121、EfficientNetB3 和 MobileNetV2。
• 集成卷积块注意力模块（CBAM），以聚焦相关特征图。
• 基于图像数据，对猴痘及其他疾病在模型间进行对比分析。
• 报告验证准确性并识别表现最佳的架构。

实验结果

研究问题

• RQ1 在多种模型中整合 CBAM 是否能提升基于图像的猴痘分类性能？
• RQ2 哪种带有 CBAM 的模型在猴痘检测中获得最高的验证准确性？
• RQ3 Xception-CBAM-Dense 架构与其他测试模型相比如何？
• RQ4 迁移学习方法在猴痘与相似皮肤病情相比的总体有效性如何？

主要发现

• Xception-CBAM-Dense 架构在验证准确率为 83.89% 时达到了最佳性能。
• 在测试的模型中，对带有 CBAM 的网络进行了猴痘分类的评估和比较。
• 该研究展示了深度迁移学习方法结合注意力机制用于疾病图像分类的潜力。