COVID-19 Detection by Using Handcrafted Features Extracted From Chest CT-Scan Images

Coronavirus illness, which was initially diagnosed in 2019 but has propagated rapidly across the globe, has led to increased fatalities. According to professional physicians who examined chest CT scans, COVID-19 behaves differently than various viral cases of pneumonia. Even though the illness only recently emerged, a number of research investigations have been performed wherein the progression of the disease impacts mostly on the lungs are identified using thoracic CT scans. In this work, automated identification of COVID-19 is used by using machine learning classifier trained on more than 1000+ lung CT Scan images. As a result, immediate diagnosis of COVID-19, which is very much necessary in the opinion of healthcare specialists, is feasible. To improve detection accuracy, the feature extraction method are applied on regions of interests. Feature extraction approaches, including Discrete Wavelet Transform (DWT), Grey Level Cooccurrence Matrix (GLCM), Grey Level Run Length Matrix (GLRLM), and Grey-Level Size Zone Matrix (GLSZM) algorithms are used. Then the classification by using Support Vector Machines (SVM) is used. The classification accuracy is assessed by using precision, specificity, accuracy, sensitivity and F-score measures. Among all feature extraction methods, the GLCM approach has given the optimum classification accuracy of 95.6%. . © 2023 IEEE.

Early detection of COVID-19 using deep learning architectures: CNN and resnet-101

The main purpose of this paper was to classify if subject has a COVID-19 or not base on CT scan. CNN and resNet-101 neural network architectures are used to identify the coronavirus. The experimental results showed that the two models CNN and resNet-101 can identify accurately the patients have COVID-19 from others with an excellent accuracy of 83.97 % and 90.05 % respectively. The results demonstrates the best ability of the used models in the current application domain. © 2022 IEEE.

Comparative Analysis of Linear Regression and Logistic Regression for the COVID-19 Detection from CT-Scans

The new emerging Coronavirus disease (COVID-19) is a pandemic disease due to its enormous infectious capability. Generally affecting the lungs, COVID-19 engenders fever, dry cough, and tiredness. However, some patients may not show symptoms. An imaging test, such as a chest X-ray or a chest CT scan, is therefore requested for reliable detection of this pneumonia type. Despite the decreasing trends both in the new and death reported cases, there is an extent need for quick, accurate, and inexpensive new methods for diagnosis. In this framework, we propose two machine learning (ML) algorithms: linear regression and logistic regression for effective COVID-19 detection in the abdominal Computed Tomography (CT) dataset. The ML methods proposed in this paper, effectively classify the data into COVID-19 and normal classes without recourse to image preprocessing or analysis. The effectiveness of these algorithms was shown through the use of the performance measures: accuracy, precision, recall, and F1-score. The best classification accuracy was obtained as 96% with logistic regression using the saga solver with no added penalty against 95.3% with linear regression. As for precision, recall, and F1-score the value of 0.89 was reached by logistic regression for all these metrics, as well as the value of 0.87 by linear regression. © 2022 IEEE.

Data Mining Techniques for Extraction and Analysis of Covid-19 Data

Artificial intelligence has played a crucial role in medical disease diagnosis. In this research, data mining techniques that included deep learning with different scenarios are presented for extraction and analysis of covid-19 data. The energy of the features is implemented and calculated from the CT scan images. A modified meta-heuristic algorithm is introduced and then used in the suggested way to determine the best and most useful features, which are based on how ants behave. Different patients with different problems are investigated and analyzed. Also, the results are compared with other studies. The results of the proposed method show that the proposed method has higher accuracy than other methods. It is concluded from the results that the most crucial features can be concentrated on during feature selection, which lowers the error rate when separating sick from healthy individuals. © 2022 IEEE.

Securing health care data through blockchain enabled collaborative machine learning.

Transferring of data in machine learning from one party to another party is one of the issues that has been in existence since the development of technology. Health care data collection using machine learning techniques can lead to privacy issues which cause disturbances among the parties and reduces the possibility to work with either of the parties. Since centralized way of information transfer between two parties can be limited and risky as they are connected using machine learning, this factor motivated us to use the decentralized way where there is no connection but model transfer between both parties will be in process through a federated way. The purpose of this research is to investigate a model transfer between a user and the client(s) in an organization using federated learning techniques and reward the client(s) for their efforts with tokens accordingly using blockchain technology. In this research, the user shares a model to organizations that are willing to volunteer their service to provide help to the user. The model is trained and transferred among the user and the clients in the organizations in a privacy preserving way. In this research, we found that the process of model transfer between user and the volunteered organizations works completely fine with the help of federated learning techniques and the client(s) is/are rewarded with tokens for their efforts. We used the COVID-19 dataset to test the federation process, which yielded individual results of 88% for contributor a, 85% for contributor b, and 74% for contributor c. When using the FedAvg algorithm, we were able to achieve a total accuracy of 82%.

Towards Understanding the COVID-19 Specific Patterns in Lung CT Scan Images using Deep CTNET Framework

Automated diagnosis of COVID-19 based on CTScan images of the lungs has caught maximum attention by many researchers in recent times. The rationale of this work is to exploit the texture patterns viz. deep learning networks so that it reduces the intra-class similarities among the patterns of COVID-19, Pneumonia and healthy class samples. The challenge of understanding the concurrence of the patterns of COVID-19 with other closely related patterns of other lung diseases is a new challenge. In this paper, a fine-tuned variational deep learning architecture named Deep CT-NET for COVID-19 diagnosis is proposed. Variation modelling to Deep CT-NET is evaluated using Resnet50, Xception, InceptionV3 and VGG19. Initially, grey level texture features are exploited to understand the correlation characteristics between these grey level patterns of COVID-19, Pneumonia and Healthy class samples. CT scan image dataset of 20,978 images was used for experimental analysis to assess the performance of Deep CT-NET viz., all mentioned models. Evaluation outcomes reveals that Resnet50, Xception, and InceptionV3 producing better performance with testing accuracy more than 96% in comparison with VGG19. © 2022 IEEE.

Classification of Lung CT Scan Images using Machine Learning

Timely discovery of COVID-19 may safeguard numerous diseased people. Several such lung diseases can turn to be life threatening. Early detection of these diseases can help in treating them at an early stage before it becomes threatening. In this paper, the proposed 3D CNN model helps in classifying the CT scans as normal and abnormal, which can then be used to treat the patients after recognizing the diseases. Chest X-ray is fewer commanding in the initial phases of the sickness, while a CT scan of the chest is advantageous even formerly symptoms seem, and CT scan accurately identify the anomalous features which are recognized in images. Besides this, using the two forms of images will raise the database. This will enhance the classification accuracy. In this paper the model used is a 3D CNN model;using this model the predictions are done. The dataset used is acquired from NKP Salve Medical Institute, Nagpur. This acquired dataset is used for prediction while an open source database is used for training the CNN model. After training the model the prediction were successfully completed, with these proposed 3D CNN model total accuracy of 87.86% is achieved. This accuracy can further be increased by using larger dataset. © 2022 IEEE.

Classification of COVID-19 CT Scan Images Using Novel Tolerance Rough Set Approach

Since December 2019, the COVID-19 outbreak has been triggering a global crisis. COVID-19 is extremely infectious and spreads quickly across the world, so early detection is essential. Chest imaging has been shown to play an important role in the progression of COVID-19 lung disease. The respiratory system is the part of the human body that is most affected by the COVID-19 virus. Images from a Chest X-ray and a Computed Tomography scan can be used to diagnose COVID-19 quickly and accurately. CT scans are preferred over X-rays to rule out other pulmonary illnesses, assist with venous entry, and pinpoint any new heart problems. Ultrasound may be useful and therapeutic, and Point-Of-Care Ultrasound (POCUS) has been used to aid in the assessment of hospitalized patients. A Novel Tolerance Rough Set Classification approach (NTRSC) is presented in this paper to classify COVID and NON-COVID CT scan images. NTRSC approach uses similarity metrics to compute the similarity between feature values. Then, NTRSC is applied on the test images which is compared with the lower approximation values. The proposed NTRSC approach is applied to predict the COVID and NON-COVID cases based on CT scan images. The outcome of the proposed algorithm produces a higher accuracy of 0.95%, 0.88%, 0.96%, and 0.93% for Gray-Level Co-occurrence Matrix (GLCM 0°, GLCM 45°, GLCM 90°, and GLCM 135°) features, respectively. The proposed classification approach experiment is compared to those of other methods such as Decision Tree classifier, Random Forest Classifier, Naive Bayes Classifier, K-Nearest Neighbor, and Support Vector Machine, to infer that the proposed approach is a less expensive way to predict and make decisions about the disease. The results show that the strength of the proposed NTRSC approach outperforms the other approaches. Using the proposed classification approach, the research indicates an improvement in diagnostic accuracy and minimum error rate. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Efficient Transfer Learning for COVID-19 Diagnosis using X-Ray and Computed Tomography Images

In this work, we aim to find an effective model to diagnose COVID-19 by using a Transfer Learning (TL) model. The purpose is to classify COVID-19 infected persons from chest X-Ray (XR) and Computed Tomography (CT) images. Several Transfer Learning models have been studied to find the most efficient and effective among them. The proposed approach is based on Tensorflow and the architecture uses the MobileNet_V2 model. The datasets that are used in this study are publicly available. In order to train and evaluate our proposed model, we collected the CT scans dataset of 8000 images with two classes of infected and normal lungs, and the XR dataset contains 616 images. Two experiments are conducted with samples of different sizes to evaluate the model using google colab. The results revealed that the performance of our model MobileNet_V2 is highest with validation accuracy for XR and CT scans images: Val_AccuracyXR =96.77% and Val_AccuracyCT =99.67%, and test time for XR and CT scans images: TXR =0.18s, tCT=0.03s respectively. © 2022 IEEE.

Convolutional Neural Network Based Feature Hybridization For Ct Scan Images: Covid-19 Detection Perspective

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Early diagnosis is only the proactive process to resist against the unwanted death. However, machine vision-based diagnosis systems show unparalleled success with higher accuracy and low false diagnosis rate. Working with the proposed method, this research has found that Computed Tomography (CT) provides more satisfactory outcomes regarding all the performance metrics. The proposed method uses a feature hybridization technique of concatenating the textural features with neural features. The literature review suggests that medical experts recommended chest CT in covid diagnosis rather than chest X-ray as well as RT-PCR. It is found that chest CT is more effective in diagnosis for being low false-negative rate. Moreover, the proposed method has used segmentation technique to dig the potential region of interest and obtain accurate features. Compared with different CNN classifier, such as, VGG-16, AlexNet, VGG-19 or ResNet50 and scratch model also. To obtain the satisfactory performance VGG-19 was used in this study. The Proposed machine learning based fusion technique achieves superior performance according to COVID-19 positive or negative with the accuracy of 98.63%, specificity of 99.08% and sensitivity of 98.18%. © 2022 IEEE.

A Hybrid Model for Covid-19 Detection using CT-Scans

Although some believe it has been wiped out, the coronavirus is striking again. Controlling this epidemic necessitates early detection of coronavirus disease. Computed tomography (CT) scan images allow fast and accurate screening for COVID-19. This study seeks to develop the most precise model for identifying and classifying COVID-19 by developing an automated approach using transfer-learning CNN models as a base. Transfer learning models like VGG16, Resnet50, and Xception are employed in this study. The VGG16 has a 98.39% accuracy, the Resnet50 has a 97.27% accuracy, and the Xception has a 96.6% accuracy;after that, a hybrid model made using the stacking ensemble method has an accuracy of 98.71%. According to the findings, hybrid architecture offers greater accuracy than a single architecture. © 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

NFNets-CNN for Classification of COVID-19 from CT Scan Images

Coronavirus disease (COVID-19) is an infectious disease caused by the coronavirus was first found in Wuhan, China in December 2019. It has infected more than 300 million people with more than 5 million of death cases. Until now, the virus is still evolving producing new variants of concern contributes to the increase the infection rate around the world. Thus, various diagnostic procedures are in need to help physicians in diagnosis disease certainly and rapidly. In this study, deep learning approach is used to classify normal and COVID-19 cases from CT scan images. Normalizer Free CNN network (NFNets) model is implemented on the images. Statistical measures such as accuracy, precision, sensitivity (also known as recall) are used to evaluate the performance of the model against the previous studies. Loss of 0.0842, accuracy of 0.7227, precision of 0.9751 and recall of 0.9727 are achieved. Thus, further optimization on the NFNets learning algorithm is required to improve the classification performanceClinical Relevance-Implementation of deep learning technique to automate diagnosis of diseases such as COVID-19 cases from CT scan images will simplify the clinical flow towards providing reliable intelligent aids for patient care. © 2022 IEEE.

Covid RayScan

The year 2020 was an unprecedented time for all, combating COVID, following precautionary measures and finding a cure for the virus was of utmost importance. As the COVID-19 is here to stay, it is imperative to detect it as early as possible. Our web application (COVID RayScan) is a prediction-based Machine Learning application which can be used by technicians, doctors at hospitals to understand a X-ray or CT-Scan and hence quickly detect if a patient suffers from Covid or not. According to NCBI, it takes 17.4 minutes for a doctor to treat every patient and that metric has increased exponentially with increase in COVID. COVID RayScan with the help of Deep Learning CNN Networks like ResNet50,VGG16,Inception and Xception helps a technician to run the X-Ray/CT-Scan image through our web application to get the desired result which in turn saves the doctor's as well as patients time and make the process much more efficient. © 2022 IEEE.

Deep Learning based Lung Segmentation Prior for Robust COVID-19 Classification

Independent of a person's race, COVID-19 is one of the most contagious diseases in the world. The World Health Organization classified the COVID-19 outbreak as a pandemic after noting its global distribution. By using (i) sample-supported analysis and (ii) image-assisted diagnosis, COVID-19 is examined and verified. Our goal is to use CT scan images to identify the COVID-19 infiltrates. The followings steps are used to carry out the suggested work: (i) Automated segmentation with CNN;(ii) Feature mining;(iii) Principal feature selection with Bat-Algorithm;(iv) Classifier implementation using mobile framework and (v) Performance evaluation. We used a variety of automatic segmentation algorithms in our experiment, and the VGG-16 produced better results. This study is evaluated using benchmark datasets gathered, and SVM based RBF kernal classifier system resulted in superior COVID-19 abnormality identification. © 2023 IEEE.

Multi-branch sustainable convolutional neural network for disease classification

Pandemic and natural disasters are growing more often, imposing even more pressure on life care services and users. There are knowledge gaps regarding how to prevent disasters and pandemics. In recent years, after heart disease, corona virus disease-19 (COVID-19), brain stroke, and cancer are at their peak. Different machine learning and deep learning-based techniques are presented to detect these diseases. Existing technique uses two branches that have been used for detection and prediction of disease accurately such as brain hemorrhage. However, existing techniques have been focused on the detection of specific diseases with double-branches convolutional neural networks (CNNs). There is a need to develop a model to detect multiple diseases at the same time using computerized tomography (CT) scan images. We proposed a model that consists of 12 branches of CNN to detect the different types of diseases with their subtypes using CT scan images and classify them more accurately. We proposed multi-branch sustainable CNN model with deep learning architecture trained on the brain CT hemorrhage, COVID-19 lung CT scans and chest CT scans with subtypes of lung cancers. Feature extracted automatically from preprocessed input data and passed to classifiers for classification in the form of concatenated feature vectors. Six classifiers support vector machine (SVM), decision tree (DT), K-nearest neighbor (K-NN), artificial neural network (ANN), naïve Bayes (NB), linear regression (LR) classifiers, and three ensembles the random forest (RF), AdaBoost, gradient boosting ensembles were tested on our model for classification and prediction. Our model achieved the best results on RF on each dataset. Respectively, on brain CT hemorrhage achieved (99.79%) accuracy, on COVID-19 lung CT scans achieved (97.61%), and on chest CT scans dataset achieved (98.77%). © 2023 Wiley Periodicals LLC.

On the Use of Conventional Neural Networks for COVID-19 Detection in CT-Scan Images: A Comparative Study and Performance Analysis

This paper presents a comprehensive study on deep learning for COVID-19 detection using CT-scan images. The proposed study investigates several Conventional Neural Networks (CNN) architectures such as AlexNet, ZFNet, VGGNet, and ResNet, and thus proposed a hybrid methodology base on merging the relevant optimized architectures considered for detecting COVID-19 from CT-scan images. The proposed methods have been assessed on real datasets, and the experimental results conducted have shown the effectiveness of the proposed methods, allowing achieving a higher accuracy up to 99%. © 2022 IEEE.

Design of an Efficient Approach for Performance Enhancement of COVID-19 Detection Using Auxiliary GoogLeNet by Using Chest CT Scan Images

In every country on this planet, COVID-19 disease s right now one of the most unsafe issues. The expedient and precise space of the Covid virus infection s major to see and take better treatment for the infected patients will increase the chance of saving their lives. The quick spread of the Covid virus has blended complete interest and caused greater than 10 lacks cases to date. To battle this spread, Chest CTs arise as a basic demonstrative contraption for the clinical association of COVID-19 related to a lung illness. A modified confirmation device is essential for assisting in the screening for COVID-19 pneumonia by making use of chest CT imaging. The COVID-19 illness detection utilizing supplementary GoogLeNet is shown in this study. Deep Convolutional Neural Networks were built by researchers at Google, and one of their innovations was the Inception Network. GoogLeNet is a 22-layer deep convolutional neural network that is a variation of the inception Network. GoogLeNet is utilized for a variety of additional computer vision applications nowadays, including face identification and recognition, adversarial training, and so on. The findings indicate that the GoogLeNet method is superior to the CNN Method in terms of its ability to detect COVID-19 sickness. © 2022 IEEE.

Various intelligent approaches for classification using CT-scan images: A Systematic Literature Review

COVID-19 has caused a devastating effect in every aspect across the world. The pandemic brought life to a standstill. Frontline workers are working day and night to treat patients and save lives. As critical is the timely and quick detection of this communicable disease, it necessitates the need for a diagnostic system that is automatic and as accurate as possible. The number of false negatives and hysteresis must be as low as possible. CT scans of the lungs can help in quicker detection of the presence of the virus as opposed to RT-PCR test. The purpose of this article is to present a survey of current scientific work on CT scan classification techniques, outlining and structuring what is currently available. We conduct a systematic literature review in which we compile and categorize the latest papers from top conferences to present a synopsis of CT scan images data classification techniques and their issues. This review identifies the present state of CT image classification research and decides where further research is needed. A review paper discusses different classification methods for CT scan images, including a comparative study of major classification techniques. © 2022 Copyright for this paper by its authors.

Detection of Covid-19 using Transfer Learning Technique

The whole world is suffering from the wave of the novel coronavirus that causes the large-scale death of a population and is proclaimed a pandemic by WHO. As RT-PCR tests to detect Coronavirus are costly and time taking. So now these days, the purpose of the researcher is to detect these diseases with the help of Artificial Intelligence or Machine learning-based models using CT scan images and X-rays images. So the testing cost, time taken and the number of data required could be minimized. In this paper, transfer learning based on three fine-tuned models has been proposed for Covid detection. The performance of these proposed fine-tuned models has been also compared with other competing models to check the accuracy and other matrices. © 2022 IEEE.

Cognitive Computing in diagnosis of Covid-19 from CT scan and radiograph images: A comparative study

The dreadful coronavirus has not only shattered the lives of millions of people, but it has also placed enormous strain on the whole healthcare system. In order to isolate positive cases and stop the disease from spreading, early detection of COVID-19 is crucial. Currently, a laboratory test (RT-PCR) on samples collected from the throat and nose is required for the official diagnosis of COVID-19. Specialized tools are needed for the RT-PCR test, which takes at least 24 hours to complete. It may often provide more false negative and false positive results than expected. Therefore, using X-ray and CT scan images of the individual's lung, COVID-19 screening can be used to support the conventional RTPCR methods for an accurate clinical diagnosis. The importance of chest imaging in the emergence of this lung illness has been recognized. Images from the computed tomography (CT) scan and chest X-ray (CXR) can be used to quickly and accurately diagnose COVID-19. However, CT scan pictures have their own drawbacks. In order to assess the effectiveness of chest imaging approaches and demonstrates that CXR as an input may compete with CT scan pictures in the diagnosis of COVID-19 infection using various CNN based models, this article thoroughly covers modern deep learning techniques (CNN). For CXR and CT scan pictures, we have evaluated with ResNet, MobileNet, VGG 16, and EfficientNet. Both chest X-ray (3604 Images) and CT scans (3227 images) from publicly accessible databases have been evaluated, and the experimental outcomes are also contrasted. © 2022 IEEE.