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Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidem...
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Context Quantitative and semiquantitative indicators of lung involvement in coronavirus disease 2019 (COVID-19) could help to stratify patients and thus help in triaging and speeding up the entire workflow in hospitals as patients with higher severity scores require early therapeutic intervention and critical care.
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Citations
... Detection of Covid-19 from Digital Chest X-Ray and CT Scan Images using Deep Learning .. Neural Network (CNN) was used to classify Covid-19 and normal cases, Good results were obtained from CNN but it has suffered from tunning of hyper parameters[84].[85] Proposed an enhanced CNN using 22 layers to build a model for classifying Covid-19 and non-Covid-19, the model was able to achieve 95.7% accuracy for Covid-19 detection, 96.7% accuracy for detecting pneumonia, and 93% for normal cases. ...
Purpose: The world was facing a global health crisis in December 2019 and World Health Organization declared a state of biological emergency on 30 January 2020 due to a newly emerged virus called SARS-CoV (Severe Acute Respiratory Syndrome Coronavirus). According to the report published by WHO, there were 761 million confirmed cases and 6.8 million deaths were reported globally as of 21 march 2023. Artificial intelligence which includes Deep learning has played a great role in tackling medical emergencies so far, Purpose of this paper is to throw light on the work done to detect Covid-19 using X-ray images and CT-Scan images, Present paper will act as trailblazing and State of Art for all the future work that needs to be done.
Methods: The main challenge for the healthcare department was to detect the virus at the earliest to give suitable treatment to the patient. The prevailing RT-PCR (Reverse Transcription-Polymer Chain Reaction) takes 2-3 days. Therefore, it was an urgent call to engage Artificial intelligence in the system for timely results. Radiological images combined with AI (Artificial Intelligence) proved to be a very powerful tool in the early detection of various diseases. We have collected and scrutinized the best papers that have proposed deep learning models. A total of 104 papers are used for the review.
Results: Promising results are given by different deep learning models, some of the models have achieved accuracy and precision of up to 100%. Novel architectures have performed better as compared with the pre-trained deep learning model in terms of accuracy but we cannot justify which model is best as dataset used by each model is different.
Conclusion: Deep analysis of the models implemented by different researchers has shown huge potential in winning the war against Covid-19. Our findings also indicate some of the challenges that need to address for the proper implementation of AI models in the medical sphere.
... This virus-caused illness impacts more than just one nation; in fact, it has caused suffering for the entire planet. A number of viral types have emerged in the last ten years, but their lifespan is often only a few days or months [2]. Few of these viruses are diagnosed despite the fact that numerous scientists are studying them and have prepared vaccines. ...
The recently identified coronavirus, or COVID-19, is a pandemic that has had a significant impact on global financial and social problems. To mediate the process of allowing prompt disease identification, a computational intelligence solution technique is necessary. Various computational intelligence techniques, including information technology, neural networks, and computational language processing, have been suggested in the literature as ways to detect the spread of the coronavirus epidemic. Furthermore, when compared to alternative techniques, the use of deep learning models has shown excellent performance. The purpose of this work is to investigate the use of deep learning and feature selection methods for the identification and characterization of coronavirus infections. The study also suggests a structure which consists of a deep learning model for feature extraction, classification, and performance evaluation. The creation of a feature fusion-based CNN architectures with an improved feature selection method is what makes this study novel.
... It has therefore shown incredible accomplishment in medical procedures as well as remedies. Computer vision uses medical imaging technologies like X-rays, CT scans, MRIs, and so on to solve a range of medical problems [14]. ...
Since the outbreak, the novel coronavirus (COVID-19) has infected more than 180 million people and has taken a toll of 3.91 million lives globally as of June 2021. This virus causes symptoms like fever, cold, and fatigue, and can develop into Pneumonia which can be detected using chest X-rays (CXRs). Therefore, early detection of COVID-19 can help get early medical attention. However, a sudden rise in the number of cases in many countries caused by COVID waves increases the burden on their testing facilities. As a result, they sometimes fail to perform enough testing to contain the spread. This work proposes a deep learning model to detect COVID-19 and Pneumonia based on CXRs. The dataset for our COVID model contains a total of 3,400 CXRs images of COVID-19 patients and 3,400 normal CXRs. The dataset for our Pneumonia model contains 1,300 CXR images of Pneumonia patients and 1,300 normal CXRs. We use convolutional neural network provided by TensorFlow to build our model, which gave 94.17% and 93.55% accuracy for COVID model and Pneumonia model, respectively. Finally, we deployed our model on the web and added a web tracker, which gives us the cases, deaths, and recoveries state-wise and nationwide.
... Meanwhile, Mahin et al. proposed a deep learning technique for COVID-19 identification from chest X-ray (CXR) data [12]. Singh et al. recommended a multi-objective approach for categorizing COVID-19 using computed tomography (CT) scan images [13]. In a similar vein, Islam suggested a CNN-based method to detect chest abnormalities indicative of COVID-19 [14]. ...
This study delves into the vital task of classifying chest X-ray (CXR) samples, particularly those related to respiratory ailments, using advanced clinical image analysis and computer-aided radiology techniques. Its primary focus is on developing a classifier to accurately identify COVID-19 cases. Through the application of machine learning and computer vision methodologies, the research aims to enhance the precision of COVID-19 detection. It investigates the effectiveness of Histogram of Oriented Gradients (HOG) feature extraction techniques in conjunction with various classifiers, such as Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), K-nearest neighbor (KNN), and Tree Bagger (TB), alongside an innovative ensemble learning approach. Results indicate impressive accuracy rates, with KNN, SVM, DT, NB, and TB all surpassing the 90% mark. However, the ensemble learning method emerges as the standout performer. By leveraging HOG features extracted from CXR images, this approach presents a robust solution for COVID-19 diagnosis, offering a powerful tool to address the diagnostic challenges posed by the pandemic.
... After preprocessing, the features were fed into a number of machine learning classifiers, including k-Nearest Neighbors (k-NN), Naive Bayes (NB) model and Random Forest (RF) model, which predicted whether or not the patient had COVID-19 infection. To identify COVID-19 in chest CT images, Singh et al. [12] suggested a differential evolution-based convolutional neural network that is multi-objective. ...
Globally, COVID-19 has impacted severely the healthcare systems and the patients as well. Reverse Transcription-Polymerase Chain Reaction (RT-PCR) tests can be effectively supplemented with computed tomography images. Recent research on CT-based screening found that COVID-19 infection is linked to abnormalities in chest Computed Tomography (CT). However, it is difficult to distinguish these from the general abnormalities that are caused in the lungs. Although COVID-19 RT-PCR testing is exceedingly precise, its sensitivity varies based on the sampling technique and the period as well. Some studies have even shown that RT-PCR testing displays very low COVID-19 sensitivity. This motivated the authors to propose a new deep-learning model called PixNet that can detect positive and negative cases accurately. We compared the effectiveness of the proposed model against several state-of-the-art models trained on CT images. On analysis, it is found that the proposed model displays 96% classification accuracy in diagnosing COVID-19 infection. The proposed algorithm automatically detects the infection owing to COVID-19 from CT scan images, which may be an effective screening tool for Clinicians.
... Ko [12]. Singh et al. recommended a multi-objective approach for categorizing COVID-19 using computed tomography (CT) scan images [13]. In a similar vein, Islam suggested a CNN-based method to detect chest abnormalities indicative of COVID-19 [14]. ...
This study delves into the vital task of classifying chest X-ray (CXR) samples, particularly those related to respiratory ailments, using advanced clinical image analysis and computer-aided radiology techniques. Its primary focus is on developing a classifier to accurately identify COVID-19 cases. Through the application of machine learning and computer vision methodologies, the research aims to enhance the precision of COVID-19 detection. It investigates the effectiveness of Histogram of Oriented Gradients (HOG) feature extraction techniques in conjunction with various classifiers, such as Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), K-nearest neighbor (KNN), and Tree Bagger (TB), alongside an innovative ensemble learning approach. Results indicate impressive accuracy rates, with KNN, SVM, DT, NB, and TB all surpassing the 90% mark. However, the ensemble learning method emerges as the standout performer. By leveraging HOG features extracted from CXR images, this approach presents a robust solution for COVID-19 diagnosis, offering a powerful tool to address the diagnostic challenges posed by the pandemic.
... It is important to stress that there are more datasets available, e.g. [16], [17], [18], [19] and [20]. ...
... Study Accuracy Detection of COVID-19 on X-rays Covid-19 image data collection [16] [23] 89.2% NIH "ChestX-ray 14" [17] [24] 94.6% Chest X-Ray Images (Pneumonia) [15] [ 25] 93.9% Detection of COVID-19 on CT scans Private dataset [27] 91.7% Covid-19 Radiography Database [8] & Chest CT-Scan images Dataset [18] [31] 93.0% SARS-COV-2 Ct-Scan [19] [ 30] 92.0% ...
Pneumonia and COVID-19 are respiratory illnesses, the last caused by the severe acute respiratory syndrome virus, coronavirus 2 (SARS-CoV-2). Tra-ditional detection processes can be slow, prone to errors, and laborious, lead-ing to potential human mistakes and a limited ability to keep up with the speed of pathogen development. A web diagnosis application to aid the phy-sician in the diagnosis process is presented, based on a modified deep neural network (AlexNet) to detect COVID-19 on X-rays and computed tomogra-phy (CT) scans as well as to detect pneumonia on X-rays. The system reached accuracy results well above 90% in seven well-known and docu-mented datasets regarding the detection of COVID-19 and Pneumonia on X-rays and COVID-19 in CT scans.
... Consequently, viruses within this category manifest distinct characteristics in radiographic images, necessitating careful consideration. Several studies have presented evidence of exposure [9][10][11][12]. ...
Computer-aided diagnosis (CAD) techniques, exemplified by chest x-ray (CXR)-based methods, offer a cost-effective alternative for early-stage COVID-19 diagnosis compared to expensive options such as polymerase chain reaction (PCR) and computed tomography (CT) scan. Despite efforts to diagnose COVID-19 with CXR-based methods, their performance could be improved by considering the spatial relationships between regions of interest (ROIs) in CXR images. This oversight hinders the ability to accurately identify areas of the human lung most vulnerable to COVID-19. This model implements a two-way classification system to differentiate between lung X-ray impressions, accurately determining whether they are affected or normal. The effectiveness of this system is assessed using metrics such as accuracy, recall, precision, and F1-score. We employed over 2409 samples of X-ray images in the COVID-19 diagnosis process. The results obtained from the VGG16 model showcase outstanding performance, with a recognition rate of 99.58% for X-ray images and 94.29% for CT-scan pictures within the given sample size and two-class categorization. This model surpasses all existing approaches documented in the literature. Medical professionals and healthcare workers can effectively utilize this proposed system, leveraging X-rays and CT scans of human lungs to identify COVID-19 cases accurately.
... In the wake of these challenges, medical imaging has assumed a critical role as a supplementary diagnostic tool, especially when combined with clinical assessments, epidemiological information, and laboratory results [6]. Among the imaging modalities, chest computed tomography (CT) scans have been paramount in the rapid identification and isolation of infected individuals, offering greater detail in soft tissue contrast than X-rays, which are more accessible but less precise [7,8]. ...
In the current landscape of the COVID-19 pandemic, the utilization of deep learning in medical imaging, especially in chest computed tomography (CT) scan analysis for virus detection, has become increasingly significant. Despite its potential, deep learning’s “black box” nature has been a major impediment to its broader acceptance in clinical environments, where transparency in decision-making is imperative. To bridge this gap, our research integrates Explainable AI (XAI) techniques, specifically the Local Interpretable Model-Agnostic Explanations (LIME) method, with advanced deep learning models. This integration forms a sophisticated and transparent framework for COVID-19 identification, enhancing the capability of standard Convolutional Neural Network (CNN) models through transfer learning and data augmentation. Our approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model generalization. The pivotal element of our methodology is the use of LIME, which demystifies the AI decision-making process, providing clinicians with clear, interpretable insights into the AI’s reasoning. This unique combination of an optimized Deep Neural Network (DNN) with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic process. Our method, validated on the SARS-COV-2 CT-Scan dataset, demonstrates exceptional diagnostic accuracy, with performance metrics that reinforce its potential for seamless integration into modern healthcare systems. This innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decision-making in the ongoing battle against COVID-19.
... Studies on automated diagnosis of COVID-19 pneumonia using CT achieved promising results [24][25][26][27]. U-Net segmentation combined with 3D CNN classification achieved 90% accuracy using non-public CT image data [28]. ...
In this paper, we present a novel approach to diagnose and localize COVID-19 using CT and X-ray images, which is based on deep transfer learning. The proposed method utilizes a pre-trained convolutional neural network (CNN) architecture and fine-tunes it on a decent-sized COVID-19 dataset. In addition, we introduce a new model weighting technique to enhance the performance of the proposed method. The experimental results demonstrate that the proposed approach outperforms existing state-of-the-art methods in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC) for both diagnosis and localization tasks. This method has the potential to aid radiologists and clinicians in accurately and quickly diagnosing COVID-19 in clinical settings.
