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.

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.

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.

COVID-19 Detection in the Images of Chest CT Scan Using K-NN in Comparison with NB Classifier to Improve the Accuracy

This chapter is about the improvisation in the accuracy in COVID-19 detection using chest CT-scan images through K-Nearest Neighbour (K-NN) compared with Naive-Bayes (NB) classifier. The sample size considered for this detection is 20, for group 1 and 2, where G-power is 0.8. The value of alpha and beta was 0.05 and 0.2 along with a confidence interval at 95%. The K-NN classifier has achieved 95.297% of higher accuracy rate when compared with Naive Bayes classifier 92.087%. The results obtained were considered to be error-free since it was having the significance value of 0.036 (p < 0.05). Therefore, in this work K-Nearest Neighbor has performed significantly better than Naive Bayes algorithm in detection of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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.

Covid Analysis Prediction Using Densenet Method in Deep Learning

Coronavirus is a quickly spreading viral sickness that infects people, yet in addition creatures as well. Clinical research of COVID-19-tainted patients revealed that these individuals are frequently infected by a lung infection as a result of their interactions with this Corona Virus Disease. Chest CT-scans images are used for diagnosing lungs related problems. Deep learning is the best method of AI, which gives valuable examination to consider a lot of chest CT-Scan pictures that can fundamentally effect on screening of Covid-19. Image and statistical data were used for the evaluation of accuracy and mean value analysis. The DenseNet method is one of the Convolutional Neural Network methods, which achieves better performance during image pre-processing and prediction. The accuracy of our proposed system is up to 95 to 97%, respectively. This kind of system helps to analyze the COVID-19 infection in its early stages. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Diagnosis of Covid-19 using Chest X-ray Images using Ensemble Model

The Coronavirus pandemic devastatingly affects worldwide social prosperity, and general well-being, deadening the human way of life all around the world and undermining our security. Due to the increasing number of confirmed cases associated with COVID-19, it is more important to identify the healthy and infected patients so the control of spread and treatment of infected patients can be done effectively. This work aims to correlate the presence of Covid-19 with the help of both chest X-ray images and CT Scan Images. Deep ensemble learning models take advantage of the different deep learning models, combine them, and produce a model with better performance. The proposed system involves Data augmentation and preprocessing of CT scan images. The same process is applied for Chest X-ray Images, compares the evaluation metrics amongst the models, and suggests the best use of CT scan and Chest X-ray for better Results and accuracy. The features extracted from the Inception V3 model are combined with the features extracted from the Xception model. The inception model convolves the same input tensor with the help of multiple filters, and the results are concatenated. The pre-trained Xception model is capable of depth-wise separable convolutions. The proposed framework works in Covid-19 diagnosis with an accuracy of 96% in Xception and 98% while combining Xception and InceptionV3 models. The final results showed that the Convolutional Neural Network Classifier built with the ensemble of Inception and Xception models that use X-ray images efficiently collects the essential features related to the infections of COVID-19. © 2023 IETE.

Feature Extraction Based on GLCM and GLRM Methods on COVID-19 Dataset

COVID-19 epidemic had devastating effects on both the economic and social infrastructures of all countries in the world. Several researches are still being carried out in order to develop effective models for the diagnosis and treatment of COVID-19 patients. A COVID-19 infected person may experience dry cough, muscle ache, brain pain, fever, sore throat, and mild to severe respiratory illness. At the same time, it has a negative impact on the lungs. The severity of COVID-19 contamination over the lungs can be examined using X-Ray and CT scan images of the chest. The examination of the severity of disease is carried out by Manual characterization. However, it may lead to human error. To overcome this drawback, an exact and proficient indicative tool is highly required. Hence, this research provides a new topology for COVID-19 diagnosis using CT scan images. In this topology, features from the images are extracted using Gray Level Co-event Matrix (GLCM), Gray Level Run Length Matrix (GRLM). An automatic classification is carried out with supervised ML algorithms. In order to examine the effectiveness of the proposed model, an experiment was carried out on the COVID-19 Dataset. Various performance evaluation metrics are utilized to identify the best ML method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.