XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks

Abstract

COVID-19 (also known as SARS-COV-2) pandemic has spread in the entire world. It is a contagious disease that easily spreads from one person in direct contact to another, classified by experts in five categories: asymptomatic, mild, moderate, severe, and critical. Already more than 66 million people got infected worldwide with more than 22 million active patients as of 5 December 2020 and the rate is accelerating. More than 1.5 million patients (approximately 2.5% of total reported cases) across the world lost their life. In many places, the COVID-19 detection takes place through reverse transcription polymerase chain reaction (RT-PCR) tests which may take longer than 48 h. This is one major reason of its severity and rapid spread. We propose in this paper a two-phase X-ray image classification called XCOVNet for early COVID-19 detection using convolutional neural Networks model. XCOVNet detects COVID-19 infections in chest X-ray patient images in two phases. The first phase pre-processes a dataset of 392 chest X-ray images of which half are COVID-19 positive and half are negative. The second phase trains and tunes the neural network model to achieve a 98.44% accuracy in patient classification.

COVID-19 Diagnosis from Chest X-ray Images Using Convolutional Neural Network(CNN) and InceptionV3

“…Abbas et al proposed a deep CNN, called Decompose, Transfer, and Compose (DeTraC), that can deal with image dataset irregularities by class boundaries investigation for the classification of COVID-19 chest X-ray images and validated it with 93.1% accuracy [21]. In [22], using CNN, a two-phase X-ray image classification (XCOVNet) was applied to detect COVID-19 infection and achieved 98.44% accuracy in classification. A CNN model was used to detect COVID-19 from chest images with an accuracy of 97.56%, and a comparison was made with two other CNN models in [23].…”

A Histogram-Based Low-Complexity Approach for the Effective Detection of COVID-19 Disease from CT and X-ray Images

“…The ML, in fact, could become a helpful and potential powerful tool for large-scale COVID-19 screening [13,65], and the author in Reference [3] has recently hypothesized that DL techniques applied to CT scans can become the first alternative screening test to the rRT-PCR in the near future. Motivated by this expectation, in the last year, the DL has been successfully used for CXRs [15,20,66,67], CT scans [56,[68][69][70], or both [18,71]. Being, indeed, challenging to summarize all the available literature in a single paper, there are some useful reviews regarding the application of DL techniques to COVID-19 detection on CXRs [72], CT scans [73,74], and both [65,75].…”

“…The approaches, which are based on segmentation, are usually founded on U-Net type architecture to identify relevant part of the CXRs/CT scans and perform classification, focusing the attention only on these sections [56,[79][80][81][82][83][84]. The second family of approaches, instead, is based on the binary classification problem of COVID/Non-COVID images [20,69,70,[85][86][87][88] and utilize deep Convolutional Neural Networks (CNNs) and their variants, including VGG16, InceptionV3, ResNet, and DenseNet.…”

“…A synoptic overview of the related work is provided in Table 1, that summarizes the main approaches pursued by the referred papers. CT [74] CT [75] CXR + CT [65] CXR + CT [76] CXR + CT [77] CXR + CT [78] CXR + CT Segmentation [79] CXR [80] CT [81] CT [82] CT [56] CT [83] CXR [84] CXR CT [85] CT [86] CT [87] CXR [20] CXR [88] CXR Unsupervised [89] Histopathological [90] CT [91] CT [92] CXR…”

“…Although DNNs have been applied with great success to identifying cases of the NCP [3,[15][16][17]20,21], they still present some challenging limitations. First of all, DL architectures have a very large number of free parameters that must be adapted by the optimization algorithms.…”