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Vision Transformer for Pneumonia Classification in X-ray Images.

PROCEEDINGS OF 2023 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION TECHNOLOGY, ICIIT 2023(2023)

FPT Univ | Univ La Rochelle | Univ Sci & Technol Hanoi

Cited 1|Views21
Abstract
Pneumonia is a common medical condition, usually caused by a lung infection, which causes the tissues in the lungs to become inflamed and affects the functioning of the lungs. Pneumonia ranges from mild pneumonia to life-threatening severity. Identifying the responsible pathogen can be difficult. Diagnosis is often based on symptoms and physical examination, which includes chest X-rays. However, the examination of chest X-rays is a challenging task and is prone to subjective variability. In this study, we focus on the research of a new image classification algorithm for classifying images indicating pneumonia pathology. The proposed method uses the Vision transformer architecture to extract data characteristics and classify the input image as pneumonia or not. Two popular deep learning architectures are compared: Vision transformer and Convolutional Neural Network. In this work, we evaluate Vit-B/16 (for Vision transformer) compared to Convolutional Neural Network algorithms such as MobileNetV2, VGG16, ResNet-50. In this study, the Vision transformer algorithm gives relatively positive classification results with an accuracy of approximately 94%.
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Key words
Convolution Neural Network,Vision Transformer,Residual Neural Network,Pneumonia,Classification
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要点】:本研究提出了一种基于Vision Transformer的肺炎X射线图像分类算法,相较于传统的卷积神经网络,在准确度上达到了约94%。

方法】:采用Vision Transformer架构提取数据特征,并与MobileNetV2、VGG16、ResNet-50等卷积神经网络结构进行比较。

实验】:在特定的数据集上进行实验,结果显示Vision Transformer在肺炎X射线图像分类上具有较高的准确性和优越性。