论文标题
通过基于变压器的方法检测胃肠道疾病
Gastrointestinal Disorder Detection with a Transformer Based Approach
论文作者
论文摘要
使用内窥镜图像进行准确的疾病分类是胃肠病学中的一个重要问题。本文介绍了一种用于协助医学诊断程序的技术，并根据使用视觉变压器和转移学习模型从内窥镜图片中对特征的特征进行分类来识别胃肠道疾病。 Vision Transformer在困难的图像分类任务上显示出非常有希望的结果。在本文中，我们提出了一种基于视觉变压器的方法来检测无线胶囊内窥镜检查（WCE）策划的结肠图像，精度为95.63 \％。我们已经将这种基于变压器的方法与验证的卷积神经网络（CNN）模型Densenet201进行了比较，并证明Vision Transformer在各种定量性能评估指标中超过了Densenet201。
Accurate disease categorization using endoscopic images is a significant problem in Gastroenterology. This paper describes a technique for assisting medical diagnosis procedures and identifying gastrointestinal tract disorders based on the categorization of characteristics taken from endoscopic pictures using a vision transformer and transfer learning model. Vision transformer has shown very promising results on difficult image classification tasks. In this paper, we have suggested a vision transformer based approach to detect gastrointestianl diseases from wireless capsule endoscopy (WCE) curated images of colon with an accuracy of 95.63\%. We have compared this transformer based approach with pretrained convolutional neural network (CNN) model DenseNet201 and demonstrated that vision transformer surpassed DenseNet201 in various quantitative performance evaluation metrics.
