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

• URL: http://arxiv.org/abs/2112.02478v1
• Date: Sun, 5 Dec 2021 05:04:38 GMT
• Title: Classification of COVID-19 on chest X-Ray images using Deep Learning model with Histogram Equalization and Lungs Segmentation
• Authors: Hitendra Singh Bhadouria, Krishan Kumar, Aman Swaraj, Karan Verma, Arshpreet Kaur, Shasvat Sharma, Ghanshyam Singh, Ashok Kumar, and Leandro Melo de Sales
• Abstract summary: We present our study based on deep learning architecture for detecting covid-19 infected lungs using chest X-rays. Our novel approach combining well-known pre-processing techniques, feature extraction methods, and dataset balancing method, lead us to an outstanding rate of recognition of 98%.
• Score: 1.6019444314820142
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background and Objective: Artificial intelligence (AI) methods coupled with biomedical analysis has a critical role during pandemics as it helps to release the overwhelming pressure from healthcare systems and physicians. As the ongoing COVID-19 crisis worsens in countries having dense populations and inadequate testing kits like Brazil and India, radiological imaging can act as an important diagnostic tool to accurately classify covid-19 patients and prescribe the necessary treatment in due time. With this motivation, we present our study based on deep learning architecture for detecting covid-19 infected lungs using chest X-rays. Dataset: We collected a total of 2470 images for three different class labels, namely, healthy lungs, ordinary pneumonia, and covid-19 infected pneumonia, out of which 470 X-ray images belong to the covid-19 category. Methods: We first pre-process all the images using histogram equalization techniques and segment them using U-net architecture. VGG-16 network is then used for feature extraction from the pre-processed images which is further sampled by SMOTE oversampling technique to achieve a balanced dataset. Finally, the class-balanced features are classified using a support vector machine (SVM) classifier with 10-fold cross-validation and the accuracy is evaluated. Result and Conclusion: Our novel approach combining well-known pre-processing techniques, feature extraction methods, and dataset balancing method, lead us to an outstanding rate of recognition of 98% for COVID-19 images over a dataset of 2470 X-ray images. Our model is therefore fit to be utilized in healthcare facilities for screening purposes.

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