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https://www.selleckchem.com/products/msc-4381.html https://www.selleckchem.com/products/msc-4381.html Lysophosphatidylglucoside is really a GPR55 -mediated chemotactic particle regarding individual monocytes and also macrophages. This paper proposes an automatic method for classifying Aortic valvular stenosis (AS) using ECG (Electrocardiogram) images by the deep learning whose training ECG images are annotated by the diagnoses given by the medical doctor who observes the echocardiograms. Besides, it explores the relationship between the trained deep learning network and its determinations, using the Grad-CAM.In this study, one-beat ECG images for 12-leads and 4-leads are generated from ECG's and train CNN's (Convolutional neural network). By applying the Grad-CAM to the trained CNN's, feature areas are detected in the early time range of the one-beat ECG image. Also, by limiting the time range of the ECG image to that of the feature area, the CNN for the 4-lead achieves the best classification performance, which is close to expert medical doctors' diagnoses.Clinical Relevance-This paper achieves as high AS classification performance as medical doctors' diagnoses based on echocardiograms by proposing an automatic method for detecting AS only using ECG.Nowadays, cancer has become a major threat to people's lives and health. Convolutional neural network (CNN) has been used for cancer early identification, which cannot achieve the desired results in some cases, such as images with affine transformation. Due to robustness to rotation and affine transformation, capsule network can effectively solve this problem of CNN and achieve the expected performance with less training data, which are very important for medical image analysis. In this paper, an enhanced capsule network is proposed for medical image classification. For the proposed capsule network, the feature decomposition module and multi-scale feature extraction module are introduced into the basic capsule network.

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