Pneumonia is life-threatening. It's critical for infants, young children, elders, and people with health problems or enfeebles immune systems. However, someone who has been infected with coronavirus can get intense Pneumonia in each lung. The best way to stumble on Pneumonia is via chest X-ray. Radiotherapist is required for an examination of chest X-Ray. An automated pneumonia detection device would be helpful for early detection in far-off places. The proposed method makes it possible to train ViT models with enhanced performance. Nowadays, ViT is an alternative method of CNN in the field of computer vision. In this research, three models have been proposed, namely convolutional neural network (CNN), VGG16, and Visual Transformer were constructed. Statistical results are obtained after the comparison of all three models. Results indicate that ViT can identify Pneumonia with an accuracy of 96.45%. And also can be used to recognize other lung-related diseases. All the models were trained and tested on a dataset that contains standard chest X-Rays and pneumonia chest X-Rays.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Detecting Pneumonia using Vision Transformer and comparing with other techniques


    Contributors:


    Publication date :

    2021-12-02


    Size :

    1014125 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Comparing active vision models

    de Croon, G. C. / Sprinkhuizen-Kuyper, I. G. / Postma, E. O. | British Library Online Contents | 2009


    Evaluation of extant computer vision techniques for detecting intruder sUAS

    Sevil, Hakki Erhan / Dogan, Atilla / Subbarao, Kamesh et al. | IEEE | 2017


    Detecting Point Merge patterns using computer vision

    Raphael, Christien / Favennec, Bruno / Hoffman, Eric G. et al. | AIAA | 2021


    DETECTING POINT MERGE PATTERNS USING COMPUTER VISION

    Raphael, Christien / Favennec, Bruno / Hoffman, Eric G. et al. | TIBKAT | 2021


    Detecting Obstacles on the Guideway Using Stereo Vision

    Ukai, M. | British Library Online Contents | 1996