Deep-learning models for Covid-19 Detection Using Chest X-Ray Images

Deep Convolutional Neural Networks are a form of neural network that can categorize, recognize, or separate images. The problem of COVID-19 detection has become the world's most complex challenge since 2019. In this research work, Chest X-Ray images are used to detect patients' Covid Positive or Negative with the help of pre-trained models: VGG16, InceptionV3, ResNet50, and InceptionResNetV2. In this paper, 821 samples are used for training, 186 samples for validation, and 184 samples are used for testing. Hybrid model InceptionResNetV2 has achieved overall maximum accuracy of 94.56% with a Recall value of 96% for normal CXR images, and a precision of 95.12% for Covid Positive images. The lowest accuracy was achieved by the ResNet50 model of 92.93% on the testing dataset, and a Recall of 93.93% was achieved for the normal images. Throughout the implementation process, it was discovered that factors like epoch had a considerable impact on the model's accuracy. Consequently, it is advised that the model be trained with a sufficient number of epochs to provide reliable classification results. The study's findings suggest that deep learning models have an excellent potential for correctly identifying the covid positive or covid negative using CXR images. © 2023 IEEE.

Classification of COVID-19 on Chest X-Ray Images Through the Fusion of HOG and LPQ Feature Sets

Covid-19 is a contagious disease that affects people's everyday life, personal health, as well as a nation's economy. COVID-19 infected individuals, according to a clinical study, are most usually contaminated with a severe condition after coming into a primary infection. The chest radiograph (also known as the chest X-ray or CXR) or a chest CT scan is a more reliable imaging method for diagnosing COVID-19 infected individuals. This article proposed a novel technique for classifying CXR scan images as healthy or affected COVID-19 by fusing the features extracted using Histogram of Oriented Gradient (HOG) and Local Phase Quantization (LPQ). This research is an experimental study that employed 7232 CXR images from a COVID-19 Radiography dataset as training and testing data. As a result, by using both individual and fused feature extraction methodologies, a developed model was created and fed into the machine learning techniques. The testing results reveal that the improved architecture outperforms current methods for identifying COVID-19 patients in terms of accuracy rate, which reached 97.15 %. © 2022 Authors. All rights reserved.

Classifying Chest X-rays for COVID-19 using Deep Learning

A developing virus called COVID-19 infects the lungs and upper layer respiratory system. Medical imaging and PCR assays can be used to identify COVID-19. Medical images are used to identify COVID-19 diseases in the proposed classification model, which works well. A crucial step in the battle against this fatal illness may turn out to be an efficient screening and diagnostic phase in treating infected sufferers. Chest X-ray (CXR) scans could be used to do this. The utilization of chest X-ray imaging for early detection may prove to be a crucial strategy in the fight against COVID-19. Many computer- aided diagnostic (CAD) methods have been developed to help radiologists and provide them with more information for the same. In a training network with many classes, tertiary classification starts to become more accurate as the number of classes increases. © 2023 IEEE.

Federated learning based Covid-19 detection.

The world is affected by COVID-19, an infectious disease caused by the SARS-CoV-2 virus. Tests are necessary for everyone as the number of COVID-19 affected individual's increases. So, the authors developed a basic sequential CNN model based on deep and federated learning that focuses on user data security while simultaneously enhancing test accuracy. The proposed model helps users detect COVID-19 in a few seconds by uploading a single chest X-ray image. A deep learning-aided architecture that can handle client and server sides efficiently has been proposed in this work. The front-end part has been developed using StreamLit, and the back-end uses a Flower framework. The proposed model has achieved a global accuracy of 99.59% after being trained for three federated communication rounds. The detailed analysis of this paper provides the robustness of this work. In addition, the Internet of Medical Things (IoMT) will improve the ease of access to the aforementioned health services. IoMT tools and services are rapidly changing healthcare operations for the better. Hopefully, it will continue to do so in this difficult time of the COVID-19 pandemic and will help to push the envelope of this work to a different extent.

Detection of Pneumonia by Binary Image Classification Using Hybrid Neural Networks

Pneumonia is a more contagious virus with worldwide health implications. If positive cases are detected early enough, spread of the pandemic sickness can be slowed. Pneumonia illness estimation is useful for identifying patients who are at risk of developing health problems. So, the conventional method like PCR kits used to detect the covid patients lead to an increase in pneumonia cases as it failed to detect at the earliest. A polymerase chain reaction (PCR) test will be performed right away on the blood or sputum to quickly identify the DNA of the bacteria that cause pneumonia. With the help of CXR images, the pneumonia is diagnosed with a high accuracy rate utilizing the HNN (Hybrid Neural Network) method. Thus, isolating them at the earlier stage and preventing the spread of disease. © 2022 IEEE.

An Empirical Study of CNN-Deep Learning Models for Detection of Covid-19 Using Chest X-Ray Images

The Covid-19 spun into a pandemic and has affected routine lives and global health. It is crucial to identify the infectious Covid-19 subjects as early as possible to avert its spread. The CXR images processed with deep learning (DL) processes have newly become an earnest method for early Covid-19 detection along with the regular RT-PCR test. This paper examines the deep learning (DL) models to detect Covid-19 from CXR images for early analysis of Covid-19. We conducted an empirical study to assess the efficacy of the proposed convolutional neural network DL model (CNN-DLM), pre-trained with some eminent networks such as MobileNet, InceptionNet-V3, ResNet50, Xception, and DenseNet121 for initial detection of Covid-19 for an openly accessible dataset. We also exhibited the accuracy and loss value curves for the selected number of epochs for all these models. The results indicate that with the proposed CNN model pre-trained with the DenseNet121 greater results were achieved compared to other pre-trained CNN-DLMs applied in a transfer learning approach. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Artificial Intelligence for the Detection of Coronavirus Disease (COVID-19) from Chest X-Ray Images

The COVID-19 pandemic keeps on devastatingly affecting the wellbeing and prosperity of the worldwide populace. To reduce the rapid spread of the COVID-19 virus primary screening of the infected patient repeatedly is a need. Medical imaging is an essential tool for faster diagnosis to fight against the virus. Early diagnosis on chest radiography shows the Coronavirus disease (COVID-19) infected images shows variations from the Normal images. Deep Convolution Neural Networks shows an outstanding performance in the medical image analysis of Computed Tomography (CT) and Chest X-Ray (CXR) images. Therefore, in this paper, we designed a Deep Convolution Neural Network that detects COVID-19 infected samples from Pneumonia and Normal Chest X-Ray (CXR) images. We also construct the dataset that contains 6023 CXR images in which 5368 images are used for training and 655 images are used for testing the model for the three categories such as COVID-19, Normal, and Pneumonia. The proposed model shows outstanding performance with 97.74% accuracy and 96% average F-Score. The results prove that the model can be used for preliminary screening of the COVID-19 infection using radiological Chest X-Ray (CXR) images to accelerate the treatment for the patients under investigation (PUI) who need it most.Copyright © 2020 Ubiquity Press. All rights reserved.

Survival prediction of COVID-19 patients using multi-modal dataset

COVID-19 has proved to be a global emergency that has fractured the healthcare systems to the extent that its impact is too challenging to encompass. Though many Computer-Aided Diagnoses (CAD) systems have been developed for automatic detection of COVID-19 from Chest X-rays and chest CT images, very few works have been done on detecting COVID-19 from a clinical dataset. Resources needed for obtaining Clinical data like blood pressure, liver disease, past traveling history, etc., are inexpensive compared to collecting Chest CT images for COVID-19 infected patients. We propose a novel multi-model dataset for the survival prediction of patients infected with COVID-19. The dataset proposed is collected and created at Mahatma Gandhi Memorial Medical College, Indore. The dataset contains clinical data and chest X-ray images obtained from the same patient infected with COVID-19. For proper prognosis of the COVID19 positive patients from the clinical dataset, we have proposed a Bi-Stream Gated Attention-based CNN (BSGA-CNN) model. The BSGA-CNN model achieved an accuracy of 96.90% (± 3.05%). A CNN based on pre-trained VGG-Net is used to classify the corresponding Chest X-Ray images. It gave an accuracy of 87.76% (± 8.78%)%. © 2022 IEEE.

COVID-19 Detection Algorithm Combining Grad-CAM and Convolutional Neural Network

In the detection of COVID-19, chest X-ray (CXR) images and CT scan images are two main technical methods, which provide an important basis for doctors' diagnosis. Currently, convolutional neural network (CNN) in detecting the COVID-19 medical radioactive images has problems of low accuracy, complex algorithms, and in¬ability to mark feature regions. In order to solve these problems, this paper proposes an algorithm combining Grad-CAM color visualization and convolutional neural network (GCCV-CNN). The algorithm can quickly classify lung X-ray images and CT scan images of COVID-19-positive patients, COVID-19-negative patients, general pneu¬monia patients and healthy people. At the same time, it can quickly locate the critical area in X-ray images and CT images. Finally, the algorithm can get more accurate detection results through the synthesis of deep learning algo¬rithms. In order to verify the effectiveness of the GCCV-CNN algorithm, experiments are performed on three COVID- 19-positive patient datasets and it is compared with existing algorithms. The results show that the classification per¬formance of the algorithm is better than the COVID-Net algorithm and the DeTraC-Net algorithm. The GCCV-CNN algorithm achieves a high accuracy of 98.06%, which is faster and more robust. © 2022, Journal of Computer Engineering and Applications Beijing Co., Ltd.;Science Press. All rights reserved.

Boosting automatic COVID-19 detection performance with self-supervised learning and batch knowledge ensembling.

PROBLEM: Detecting COVID-19 from chest X-ray (CXR) images has become one of the fastest and easiest methods for detecting COVID-19. However, the existing methods usually use supervised transfer learning from natural images as a pretraining process. These methods do not consider the unique features of COVID-19 and the similar features between COVID-19 and other pneumonia. AIM: In this paper, we want to design a novel high-accuracy COVID-19 detection method that uses CXR images, which can consider the unique features of COVID-19 and the similar features between COVID-19 and other pneumonia. METHODS: Our method consists of two phases. One is self-supervised learning-based pertaining; the other is batch knowledge ensembling-based fine-tuning. Self-supervised learning-based pretraining can learn distinguished representations from CXR images without manually annotated labels. On the other hand, batch knowledge ensembling-based fine-tuning can utilize category knowledge of images in a batch according to their visual feature similarities to improve detection performance. Unlike our previous implementation, we introduce batch knowledge ensembling into the fine-tuning phase, reducing the memory used in self-supervised learning and improving COVID-19 detection accuracy. RESULTS: On two public COVID-19 CXR datasets, namely, a large dataset and an unbalanced dataset, our method exhibited promising COVID-19 detection performance. Our method maintains high detection accuracy even when annotated CXR training images are reduced significantly (e.g., using only 10% of the original dataset). In addition, our method is insensitive to changes in hyperparameters. CONCLUSION: The proposed method outperforms other state-of-the-art COVID-19 detection methods in different settings. Our method can reduce the workloads of healthcare providers and radiologists.

A residual network-based framework for COVID-19 detection from CXR images.

In late 2019, a new Coronavirus disease (COVID-19) appeared in Wuhan, Hubei Province, China. The virus began to spread throughout many countries, affecting a large population. Polymerase chain reaction is currently being utilized to diagnose COVID-19 in suspected patients; however, its sensitivity is quite low. The researchers also developed automated approaches for reliably and timely identifying COVID-19 from X-ray images. However, traditional machine learning-based image classification algorithms necessitate manual image segmentation and feature extraction, which is a time-consuming task. Due to promising results and robust performance, Convolutional Neural Network (CNN)-based techniques are being used widely to classify COVID-19 from Chest X-rays (CXR). This study explores CNN-based COVID-19 classification methods. A series of experiments aimed at COVID-19 detection and classification validates the viability of our proposed framework. Initially, the dataset is preprocessed and then fed into two Residual Network (ResNet) architectures for deep feature extraction, such as ResNet18 and ResNet50, whereas support vector machines with its multiple kernels, including Quadratic, Linear, Gaussian and Cubic, are used to classify these features. The experimental results suggest that the proposed framework efficiently detects COVID-19 from CXR images. The proposed framework obtained the best accuracy of 97.3% using ResNet50.

Real-time COVID-19 detection over chest x-ray images in edge computing.

Severe Coronavirus Disease 2019 (COVID-19) has been a global pandemic which provokes massive devastation to the society, economy, and culture since January 2020. The pandemic demonstrates the inefficiency of superannuated manual detection approaches and inspires novel approaches that detect COVID-19 by classifying chest x-ray (CXR) images with deep learning technology. Although a wide range of researches about bran-new COVID-19 detection methods that classify CXR images with centralized convolutional neural network (CNN) models have been proposed, the latency, privacy, and cost of information transmission between the data resources and the centralized data center will make the detection inefficient. Hence, in this article, a COVID-19 detection scheme via CXR images classification with a lightweight CNN model called MobileNet in edge computing is proposed to alleviate the computing pressure of centralized data center and ameliorate detection efficiency. Specifically, the general framework is introduced first to manifest the overall arrangement of the computing and information services ecosystem. Then, an unsupervised model DCGAN is employed to make up for the small scale of data set. Moreover, the implementation of the MobileNet for CXR images classification is presented at great length. The specific distribution strategy of MobileNet models is followed. The extensive evaluations of the experiments demonstrate the efficiency and accuracy of the proposed scheme for detecting COVID-19 over CXR images in edge computing.

Automated multi-class classification of lung diseases from CXR-images using pre-trained convolutional neural networks

According to the World Health Organization (WHO), Pneumonia, COVID-19, Tuberculosis, and Pneumothorax are the leading death causes in the world. Coughing, sneezing, fever, and shortness of breath are common symptoms. To detect them, several tests such as molecular tests (RT-PCR), antigen tests, Monteux tuberculin skin test (TST), and complete blood count (CBC) tests are needed. But these are time-consuming processes and have an error rate of 20% and a sensitivity of 80%. So, radiographic tests like computed tomography (CT) and an X-ray are used to identify lung diseases with the help of a physician. But the risk of these lung diseases' diagnoses overlapping features in chest radiographs is a worry with chest X-ray or CT-scan images. To accurately classify one of four diseases with healthy images demands the automation of such a process. There is no method for identifying and categorizing these lung diseases. As a result, we were encouraged to use eight pre-trained convolutional neural networks (CNN) to classify various lung diseases into COVID-19, pneumonia, pneumothorax, tuberculosis, and normal images from the chest X-ray image dataset. This classification process is divided into two phases. In the training phase, the CNNs are trained with the Adam optimizer with a maximum epoch of 30 and a mini-batch size of 32. In the classification phase, these trained networks are used to classify diseases. In both phases, the dataset is color preprocessed, resized, and undergoes data augmentation. For this, we used eight pre-trained CNNs: Alexnet,Darknet-19, Darknet-53, Densenet-201, Googlenet, InceptionResnetV2, MobilenetV2, and Resnet-18. Finally, we concluded that the best one to classify these diseases. Among these networks, Densenet-201, achieved the highest accuracy of 97.2%, 94.28% of sensitivity, and 97.92% of specificity for K=5. For K=10, it achieved 97.49% of accuracy, 95.57% of sensitivity, and 97.96% of specificity and for K=15, achieved 97.01% of accuracy, 96.71% of sensitivity, and 97.17% of specificity. Hence, the proposed method outperformed the existing state-of-the-art methods. Finally, our proposed research could aid clinicians in making quick conclusions concerning lung problems so that treatment can proceed. © 2022 Elsevier Ltd

COVID-19 Detection Algorithm Combining Grad-CAM and Convolutional Neural Network

In the detection of COVID-19, chest X-ray (CXR) images and CT scan images are two main technical methods, which provide an important basis for doctors' diagnosis. Currently, convolutional neural network (CNN) in detecting the COVID-19 medical radioactive images has problems of low accuracy, complex algorithms, and in¬ability to mark feature regions. In order to solve these problems, this paper proposes an algorithm combining Grad-CAM color visualization and convolutional neural network (GCCV-CNN). The algorithm can quickly classify lung X-ray images and CT scan images of COVID-19-positive patients, COVID-19-negative patients, general pneu¬monia patients and healthy people. At the same time, it can quickly locate the critical area in X-ray images and CT images. Finally, the algorithm can get more accurate detection results through the synthesis of deep learning algo¬rithms. In order to verify the effectiveness of the GCCV-CNN algorithm, experiments are performed on three COVID- 19-positive patient datasets and it is compared with existing algorithms. The results show that the classification per¬formance of the algorithm is better than the COVID-Net algorithm and the DeTraC-Net algorithm. The GCCV-CNN algorithm achieves a high accuracy of 98.06%, which is faster and more robust. © 2022, Journal of Computer Engineering and Applications Beijing Co., Ltd.;Science Press. All rights reserved.

The Capacity of Artificial Intelligence in COVID-19 Response: A Review in Context of COVID-19 Screening and Diagnosis.

Artificial intelligence (AI) has been shown to solve several issues affecting COVID-19 diagnosis. This systematic review research explores the impact of AI in early COVID-19 screening, detection, and diagnosis. A comprehensive survey of AI in the COVID-19 literature, mainly in the context of screening and diagnosis, was observed by applying the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Data sources for the years 2020, 2021, and 2022 were retrieved from google scholar, web of science, Scopus, and PubMed, with target keywords relating to AI in COVID-19 screening and diagnosis. After a comprehensive review of these studies, the results found that AI contributed immensely to improving COVID-19 screening and diagnosis. Some proposed AI models were shown to have comparable (sometimes even better) clinical decision outcomes, compared to experienced radiologists in the screening/diagnosing of COVID-19. Additionally, AI has the capacity to reduce physician work burdens and fatigue and reduce the problems of several false positives, associated with the RT-PCR test (with lower sensitivity of 60-70%) and medical imaging analysis. Even though AI was found to be timesaving and cost-effective, with less clinical errors, it works optimally under the supervision of a physician or other specialists.

SVD-CLAHE boosting and balanced loss function for Covid-19 detection from an imbalanced Chest X-Ray dataset.

Covid-19 disease has had a disastrous effect on the health of the global population, for the last two years. Automatic early detection of Covid-19 disease from Chest X-Ray (CXR) images is a very crucial step for human survival against Covid-19. In this paper, we propose a novel data-augmentation technique, called SVD-CLAHE Boosting and a novel loss function Balanced Weighted Categorical Cross Entropy (BWCCE), in order to detect Covid 19 disease efficiently from a highly class-imbalanced Chest X-Ray image dataset. Our proposed SVD-CLAHE Boosting method is comprised of both oversampling and under-sampling methods. First, a novel Singular Value Decomposition (SVD) based contrast enhancement and Contrast Limited Adaptive Histogram Equalization (CLAHE) methods are employed for oversampling the data in minor classes. Simultaneously, a Random Under Sampling (RUS) method is incorporated in major classes, so that the number of images per class will be more balanced. Thereafter, Balanced Weighted Categorical Cross Entropy (BWCCE) loss function is proposed in order to further reduce small class imbalance after SVD-CLAHE Boosting. Experimental results reveal that ResNet-50 model on the augmented dataset (by SVD-CLAHE Boosting), along with BWCCE loss function, achieved 95% F1 score, 94% accuracy, 95% recall, 96% precision and 96% AUC, which is far better than the results by other conventional Convolutional Neural Network (CNN) models like InceptionV3, DenseNet-121, Xception etc. as well as other existing models like Covid-Lite and Covid-Net. Hence, our proposed framework outperforms other existing methods for Covid-19 detection. Furthermore, the same experiment is conducted on VGG-19 model in order to check the validity of our proposed framework. Both ResNet-50 and VGG-19 model are pre-trained on the ImageNet dataset. We publicly shared our proposed augmented dataset on Kaggle website (https://www.kaggle.com/tr1gg3rtrash/balanced-augmented-covid-cxr-dataset), so that any research community can widely utilize this dataset. Our code is available on GitHub website online (https://github.com/MrinalTyagi/SVD-CLAHE-and-BWCCE).

COVID-19 Detection in CXR Image Using High Frequency Emphasis Filtering Based Convolutional Neural Network

As a huge disaster for humanity, the COVID-19 has caused many negative effects on the lives of people around the world with a rapid growth. Moreover, the global pandemic of Neocoronavirushas produced many mutated strains. Although the most commonly used test for COVID-19 is reverse transcription-polymerase chain reaction (RT-PCR), CXR becomes an irreplaceable tool for the diagnosis and analysis for a more complete and accurate visualization of the lung lesion process. Therefore, it is of high value for classification and identification studies. In this paper, the high-frequency emphasis filtering based convolutional neural networks (HFEF-CNN) are proposed for solving the automatic detection of COVID-19. Firstly, the HFEF is used to denoise the image data to make some features in the image more obvious. Then some major CNNs are used to train image classification models to achieve better detection performance. Finally, Some experiments are conducted on the 'COVID-19 Chest X-Ray Database' dataset. To verify the effectiveness of the HFEF-CNN, a histogram equalization based CNN (HE-CNN) and a restricted contrast adaptive histogram equalization based CNN (CLAHE-CNN) are compared. The experimental results show that the HFEF-CNN outperformed the above two methods. © 2022 IEEE.

Automated multi-class classification of lung diseases from CXR-images using pre-trained convolutional neural networks

According to the World Health Organization (WHO), Pneumonia, COVID-19, Tuberculosis, and Pneumothorax are the leading death causes in the world. Coughing, sneezing, fever, and shortness of breath are common symptoms. To detect them, several tests such as molecular tests (RT-PCR), antigen tests, Monteux tuberculin skin test (TST), and complete blood count (CBC) tests are needed. But these are time-consuming processes and have an error rate of 20% and a sensitivity of 80%. So, radiographic tests like computed tomography (CT) and an X-ray are used to identify lung diseases with the help of a physician. But the risk of these lung diseases’ diagnoses overlapping features in chest radiographs is a worry with chest X-ray or CT-scan images. To accurately classify one of four diseases with healthy images demands the automation of such a process. There is no method for identifying and categorizing these lung diseases. As a result, we were encouraged to use eight pre-trained convolutional neural networks (CNN) to classify various lung diseases into COVID-19, pneumonia, pneumothorax, tuberculosis, and normal images from the chest X-ray image dataset. This classification process is divided into two phases. In the training phase, the CNNs are trained with the Adam optimizer with a maximum epoch of 30 and a mini-batch size of 32. In the classification phase, these trained networks are used to classify diseases. In both phases, the dataset is color preprocessed, resized, and undergoes data augmentation. For this, we used eight pre-trained CNNs: Alexnet,Darknet-19, Darknet-53, Densenet-201, Googlenet, InceptionResnetV2, MobilenetV2, and Resnet-18. Finally, we concluded that the best one to classify these diseases. Among these networks, Densenet-201,achieved the highest accuracy of 97.2%, 94.28% of sensitivity, and 97.92% of specificity for K=5. For K=10, it achieved 97.49% of accuracy, 95.57% of sensitivity, and 97.96% of specificity and for K=15, achieved 97.01% of accuracy, 96.71% of sensitivity, and 97.17% of specificity. Hence, the proposed method outperformed the existing state-of-the-art methods. Finally, our proposed research could aid clinicians in making quick conclusions concerning lung problems so that treatment can proceed.

Annotations of Lung Abnormalities in Shenzhen Chest X-ray Dataset for Computer-Aided Screening of Pulmonary Diseases.

Developments in deep learning techniques have led to significant advances in automated abnormality detection in radiological images and paved the way for their potential use in computer-aided diagnosis (CAD) systems. However, the development of CAD systems for pulmonary tuberculosis (TB) diagnosis is hampered by the lack of training data that is of good visual and diagnostic quality, of sufficient size, variety, and, where relevant, containing fine region annotations. This study presents a collection of annotations/segmentations of pulmonary radiological manifestations that are consistent with TB in the publicly available and widely used Shenzhen chest X-ray (CXR) dataset made available by the U.S. National Library of Medicine and obtained via a research collaboration with No. 3. People's Hospital Shenzhen, China. The goal of releasing these annotations is to advance the state-of-the-art for image segmentation methods toward improving the performance of fine-grained segmentation of TB-consistent findings in digital Chest X-ray images. The annotation collection comprises the following: 1) annotation files in JSON (JavaScript Object Notation) format that indicate locations and shapes of 19 lung pattern abnormalities for 336 TB patients; 2) mask files saved in PNG format for each abnormality per TB patient; 3) a CSV (comma-separated values) file that summarizes lung abnormality types and numbers per TB patient. To the best of our knowledge, this is the first collection of pixel-level annotations of TB-consistent findings in CXRs. Dataset: https://data.lhncbc.nlm.nih.gov/public/Tuberculosis-Chest-X-ray-Datasets/Shenzhen-Hospital-CXR-Set/Annotations/index.html.

COVID-19 detection through convolutional neural networks and chest X-ray images

To break the chain of COVID-19, a powerful and fast screening system is required which identifies the COVID-19 affected cases quickly such that the appropriate measures like quarantine or treatment can be taken. The traditional RT-PCR test is found to have larger misclassification rate. It also consumes more time to get the result. To solve this problem, in this paper, we have introduced a new model for COVID-19 detection based on chest X-ray (CXR) images and convolutional neural networks (CNNs). The proposed model is an automatic detection model, which considers the CXR image as input and performs an in-depth analysis to discover the COVID-19. The proposed CNN model is a very simple and effective, which is composed of five convolutional layers and three pooling layers. Every convolutional layer has different sized filters and different number of filters, which extracts all the possible features from CXR image. Simulation experiments are conducted over a newly constructed dataset based on the publicly available CXR (both COVID-19 and non-COVID-19) images. Simulation is done under two phases;3-class and 2-class and obtained an average accuracy of 92.22% and 94.44% respectively. Thus, the average accuracy is measured as 93.33%.