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Background and Objective: Artificial intelligence (AI) methods coupled withbiomedical analysis has a critical role during pandemics as it helps to releasethe overwhelming pressure from healthcare systems and physicians. As theongoing COVID-19 crisis worsens in countries having dense populations andinadequate testing kits like Brazil and India, radiological imaging can act asan important diagnostic tool to accurately classify covid-19 patients andprescribe the necessary treatment in due time. With this motivation, we presentour study based on deep learning architecture for detecting covid-19 infectedlungs using chest X-rays. Dataset: We collected a total of 2470 images forthree different class labels, namely, healthy lungs, ordinary pneumonia, andcovid-19 infected pneumonia, out of which 470 X-ray images belong to thecovid-19 category. Methods: We first pre-process all the images using histogramequalization techniques and segment them using U-net architecture. VGG-16network is then used for feature extraction from the pre-processed images whichis further sampled by SMOTE oversampling technique to achieve a balanceddataset. Finally, the class-balanced features are classified using a supportvector machine (SVM) classifier with 10-fold cross-validation and the accuracyis evaluated. Result and Conclusion: Our novel approach combining well-knownpre-processing techniques, feature extraction methods, and dataset balancingmethod, lead us to an outstanding rate of recognition of 98% for COVID-19images over a dataset of 2470 X-ray images. Our model is therefore fit to beutilized in healthcare facilities for screening purposes.

Classification of COVID-19 on chest X-Ray images using Deep Learning model with Histogram Equalization and Lungs Segmentation