A Novel Approach to Detect Abnormal Chest X-rays of COVID-19 Patients Using Image Processing and Deep Learning
نویسندگان
چکیده
The study proposes a novel approach to automate classifying Chest X-ray (CXR) images of COVID-19 positive patients. All acquired have been pre-processed with Simple Median Filter (SMF) and Gaussian (GF) kernel size (5, 5). better filter is then identified by comparing Mean Squared Error (MSE) Peak Signal-to-Noise Ratio (PSNR) denoised images. Canny's edge detection has applied find the Region Interest (ROI) on Eigenvalues [-2, 2] Hessian matrix (5 × 5) ROIs are extracted, which constitutes 'input' dataset Feed Forward Neural Network (FFNN) classifier, developed in this study. Eighty percent data used for training said network after 10-fold cross-validation performance tested remaining 20% data. Finally, validation made another set 'raw' normal abnormal CXRs. Precision, Recall, Accuracy, Computational time complexity (Big(O)) classifier estimated examine its performance.
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ژورنال
عنوان ژورنال: Artificial intelligence evolution
سال: 2021
ISSN: ['2717-5944', '2717-5952']
DOI: https://doi.org/10.37256/aie.222021977