As people's work pressure gradually increases, more and more people are at risk of getting sick. Doctors use CT images to diagnose the patient's disease. Since there are hundreds of CT images for a complete disease and the key CT images used to illustrate the disease are mainly a few. That is why the problem of image redundancy is highlighted. As a result, the workload of doctors has greatly increased. Therefore, solutions are urgently needed to help quickly identify medical CT images. This paper studies medical image recognition based on convolutional neural networks. Taking dental CT images as an example, the image which is most similar to the key template image is identified through the improved network model of fused feature vectors. Based on this, a platform suitable for all medical CT image recognition has been initially implemented.


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    Title :

    Medical Image Recognition Based on Improved Convolutional Neural Network


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wang, Yi (editor) / Martinsen, Kristian (editor) / Yu, Tao (editor) / Wang, Kesheng (editor) / Zhou, Chuanhong (author) / Zhang, Yiyang (author) / Yang, Lihua (author)

    Conference:

    International Workshop of Advanced Manufacturing and Automation ; 2020 ; Zhanjiang, China October 12, 2020 - October 13, 2020



    Publication date :

    2021-01-23


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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