论文标题
使用深度学习模型和注意机制对人类蒙基毒疾病进行分类
Classification of Human Monkeypox Disease Using Deep Learning Models and Attention Mechanisms
论文作者
论文摘要
随着世界仍在试图重建与19号病毒的广泛覆盖范围所造成的破坏，并且最近在许多国家，人类猴摩托病爆发的令人震惊的激增也威胁到新的全球大流行。人类猴摩托病综合征与水痘非常相似，麻疹经典症状，具有非常复杂的差异，例如皮肤水泡，它们以各种形式出现。各种深度学习方法在基于图像的COVID-19，肿瘤细胞和皮肤病分类任务的基于图像的诊断中表现出了有希望的表演。在本文中，我们试图将基于深层学习的方法以及卷积块注意模块（CBAM）集中在特征图的相关部分，以进行基于图像的人类Monkeypox疾病的分类。我们实施了五个深度学习模型，即VGG19，Xpection，densenet121，ExtricNetB3和MobilenetV2，以及集成的通道和空间注意机制，并进行了比较分析。由Xception-CBAM致密层组成的结构比其他模型的构建更好，在对人类Monkeypox和其他疾病进行分类的验证精度为83.89％方面。
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%.
