A Novel Approach to Detect Abnormal Chest X-rays of COVID-19 Patients Using Image Processing and Deep Learning

Authors

  • Subhagata Chattopadhyay Department of Computer Science and Engineering, GITAM School of Technology, GITAM (Deemed to be) University, Bengaluru 561203 Karnataka India

DOI:

https://doi.org/10.37256/aie.222021977

Keywords:

COVID-19, simple median filter, Gaussian filter, Canny's edge detection, region of interest, Hessian matrix, eigenvalues, Feed-Forward Neural Network

Abstract

The study proposes a novel approach to automate classifying Chest X-ray (CXR) images of COVID-19 positive patients. All acquired images have been pre-processed with Simple Median Filter (SMF) and Gaussian Filter (GF) with kernel size (5, 5). The better filter is then identified by comparing Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) of denoised images. Canny's edge detection has been applied to find the Region of Interest (ROI) on denoised images. Eigenvalues [-2, 2] of the Hessian matrix (5 × 5) of the ROIs are then extracted, which constitutes the 'input' dataset to the Feed Forward Neural Network (FFNN) classifier, developed in this study. Eighty percent of the data is used for training the said network after 10-fold cross-validation and the performance of the network is tested with the remaining 20% of the data. Finally, validation has been made on another set of 'raw' normal and abnormal CXRs. Precision, Recall, Accuracy, and Computational time complexity (Big(O)) of the classifier are then estimated to examine its performance.

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Published

2021-07-20

How to Cite

1.
Chattopadhyay S. A Novel Approach to Detect Abnormal Chest X-rays of COVID-19 Patients Using Image Processing and Deep Learning. Artificial Intelligence Evolution [Internet]. 2021 Jul. 20 [cited 2024 Dec. 7];2(2):23-41. Available from: https://ojs.wiserpub.com/index.php/AIE/article/view/977