Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model

Abstract

was first identified in December 2019 at Wuhan, China. At present, the outbreak of COVID-19 pandemic has resulted in severe consequences on both economic and social infrastructures of the developed and developing countries. Several studies have been conducted and ongoing still to design efficient models for diagnosis and treatment of COVID-19 patients. The traditional diagnostic models that use reverse transcription-polymerase chain reaction (rt-qPCR) is a costly and time-consuming process. So, automated COVID-19 diagnosis using Deep Learning (DL) models becomes essential. The primary intention of this study is to design an effective model for diagnosis and classification of COVID-19. This research work introduces an automated COVID-19 diagnosis process using Convolutional Neural Network (CNN) with a fusion-based feature extraction model, called FM-CNN. FM-CNN model has three major phases namely, pre-processing, feature extraction, and classification. Initially, Wiener Filtering (WF)-based preprocessing is employed to discard the noise that exists in input chest X-Ray (CXR) images. Then, the pre-processed images undergo fusion-based feature extraction model which is a combination of Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRM), and Local Binary Patterns (LBP). In order to determine the optimal subset of features, Particle Swarm Optimization (PSO) algorithm is employed. At last, CNN is deployed as a classifier to identify the existence of binary and multiple classes of CXR images. In order to validate the proficiency of the proposed FM-CNN model in terms of its diagnostic performance, extension experimentation was carried out upon CXR dataset. As per the results attained from simulation, FM-CNN model classified multiple classes with the maximum sensitivity of 97.22%, specificity of 98.29%, accuracy of 98.06%, and F-measure of 97.93%.

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“… 70%:30% Hold-out ImageNet Supervised Resizing, Converting to Color Image COVID-19 from Other Pneumonia NA E‑DiCoNet ELM NA Acc=94.07,Sen=98.15, Spec=91.48 [91] X-ray Combination of Three Different DBs 543 COVID_19, 600 Normal, 600 Pneumonia Img. 1220-523 Hold-out ImageNet Supervised Resizing COVID-19 from Other Pneumonia NA AlexNet, ReliefF SVM NA Acc=98.64,Spec=98.64, Sen=98.64, F-score=98.63 [92] X-ray chest X-ray (CXR) dataset 27 Normal, 220 SARS, 17 Streptococcus Img. 80%:20% 5-fold ImageNet Supervised Noise Removal by Wiener Filtering COVID-19 from Other Pneumonia NA FM-CNN MLP NA Acc=98.06,Spec=98.29, Sen=97.22, F-score=97.93 [93] X-ray Combination of Different DBs 423 COVID-19, 1341 Normal, 1345 Viral PNA Img.…”

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“…GLCM is a general statistical method with spatial connection between the pixels 28 . Being a 2D histogram, it defines the importance of a pixel by particular distance $${d}_{\mathrm{occ}}$$ in an image.…”

“…GLCM is a general statistical method with spatial connection between the pixels. 28 Being a 2D histogram, it defines the importance of a pixel by particular distance d occ in an image. Assume I i j ( , ) as the size of image, N M × , with L gray levels where I a b ( , ) 1 1 and I a b ( , ) 2 2 are two pixels with gray-level intensities, that is, x 1 and x 2 , respectively.…”

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