CCBlock: an effective use of deep learning for automatic diagnosis of COVID-19 using X-ray images

Challenges, opportunities, and advances related to COVID-19 classification based on deep learning

The novel coronavirus disease, or COVID-19, is a hazardous disease. It is endangering the lives of many people living in more than two hundred countries. It directly affects the lungs. In general, two main imaging modalities: - computed tomography (CT) and chest x-ray (CXR) are used to achieve a speedy and reliable medical diagnosis. Identifying the coronavirus in medical images is exceedingly difficult for diagnosis, assessment, and treatment. It is demanding, time-consuming, and subject to human mistakes. In biological disciplines, excellent performance can be achieved by employing artificial intelligence (AI) models. As a subfield of AI, deep learning (DL) networks have drawn considerable attention than standard machine learning (ML) methods. DL models automatically carry out all the steps of feature extraction, feature selection, and classification. This study has performed comprehensive analysis of coronavirus classification using CXR and CT imaging modalities using DL architectures. Additionally, we have discussed how transfer learning is helpful in this regard. Finally, the problem of designing and implementing a system using computer-aided diagnostic (CAD) to find COVID-19 using DL approaches is highlighted a future research possibility.

LCSB-inception: Reliable and effective light-chroma separated branches for Covid-19 detection from chest X-ray images

According to the World Health Organization, an estimate of more than five million infections and 355,000 deaths have been recorded worldwide since the emergence of the coronavirus disease (COVID-19). Various researchers have developed interesting and effective deep learning frameworks to tackle this disease. However, poor feature extraction from the Chest X-ray images and the high computational cost of the available models impose difficulties to an accurate and fast Covid-19 detection framework. Thus, the major purpose of this study is to offer an accurate and efficient approach for extracting COVID-19 features from chest X-rays that is also less computationally expensive than earlier research. To achieve the specified goal, we explored the Inception V3 deep artificial neural network. This study proposed LCSB-Inception; a two-path (L and AB channel) Inception V3 network along the first three convolutional layers. The RGB input image is first transformed to CIE LAB coordinates (L channel which is aimed at learning the textural and edge features of the Chest X-Ray and AB channel which is aimed at learning the color variations of the Chest X-ray images). The L achromatic channel and the AB channels filters are set to 50%L-50%AB. This method saves between one-third and one-half of the parameters in the divided branches. We further introduced a global second-order pooling at the last two convolutional blocks for more robust image feature extraction against the conventional max-pooling. The detection accuracy of the LCSB-Inception is further improved by employing the Contrast Limited Adaptive Histogram Equalization (CLAHE) image enhancement technique on the input image before feeding them to the network. The proposed LCSB-Inception network is experimented on using two loss functions (Categorically smooth loss and categorically Cross-entropy) and two learning rates whereas Accuracy, Precision, Sensitivity, Specificity F1-Score, and AUC Score were used for evaluation via the chestX-ray-15k (Data_1) and COVID-19 Radiography dataset (Data_2). The proposed models produced an acceptable outcome with an accuracy of 0.97867 (Data_1) and 0.98199 (Data_2) according to the experimental findings. In terms of COVID-19 identification, the suggested models outperform conventional deep learning models and other state-of-the-art techniques presented in the literature based on the results.

Comprehensive Survey of Machine Learning Systems for COVID-19 Detection

The last two years are considered the most crucial and critical period of the COVID-19 pandemic affecting most life aspects worldwide. This virus spreads quickly within a short period, increasing the fatality rate associated with the virus. From a clinical perspective, several diagnosis methods are carried out for early detection to avoid virus propagation. However, the capabilities of these methods are limited and have various associated challenges. Consequently, many studies have been performed for COVID-19 automated detection without involving manual intervention and allowing an accurate and fast decision. As is the case with other diseases and medical issues, Artificial Intelligence (AI) provides the medical community with potential technical solutions that help doctors and radiologists diagnose based on chest images. In this paper, a comprehensive review of the mentioned AI-based detection solution proposals is conducted. More than 200 papers are reviewed and analyzed, and 145 articles have been extensively examined to specify the proposed AI mechanisms with chest medical images. A comprehensive examination of the associated advantages and shortcomings is illustrated and summarized. Several findings are concluded as a result of a deep analysis of all the previous works using machine learning for COVID-19 detection, segmentation, and classification.

CheXImageNet: a novel architecture for accurate classification of Covid-19 with chest x-ray digital images using deep convolutional neural networks

Many countries around the world have been influenced by Covid-19 which is a serious virus as it gets transmitted by human communication. Although, its syndrome is quite similar to the ordinary flu. The critical step involved in Covid-19 is the initial screening or testing of the infected patients. As there are no special detection tools, the demand for such diagnostic tools has been increasing continuously. So, it is eminently admissible to find out positive cases of this disease at the earliest so that the spreading of this dangerous virus can be controlled. Although, some methods for the detection of Covid-19 patients are available, which are performed upon respiratory based samples and among them, a critical approach for treatment is radiologic imaging or X-ray imaging. The latest conclusions obtained from X-ray digital imaging based algorithms and techniques recommend that such type of digital images may consist of significant facts regarding the SARS-CoV-2 virus. The utilization of Deep Neural Networks based methodologies clubbed with digital radiological imaging has been proved useful for accurately identifying this disease. This could also be adjuvant in conquering the problem of dearth of competent physicians in far-flung areas. In this paper, a CheXImageNet model has been introduced for detecting Covid-19 disease by using digital images of Chest X-ray with the help of an openly accessible dataset. Experiments for both binary class and multi-class have been performed in this work for benchmarking the effectiveness of the proposed work. An accuracy of 100\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}% is reported for both binary classification (having cases of Covid-19 and Normal X-Ray) and classification for three classes (including cases of Covid-19, Normal X-Ray and, cases of Pneumonia disease) respectively.