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.

Mathematical Morphology Adopted Automatic Detection of Ground Glass Opacities in Lung CT Images of COVID-19 Patients

The severity of the nCOVID infection relies on the presence of Ground Glass Opacities (GGO) present in the patient's chest CT scan images. Although, detecting and delineating the precise boundaries of GGO in the chest CT images is challenging. Here, we proposed a fast and novel technique to automatically segment the regions containing GGO in lung CT images using mathematical morphology. We have tested our algorithm on the chest CT images of 145 Covid-positive cases. This unique segmentation approach correctly segments the lung field from chest CT images and identifies GGO with average sensitivity, specificity, and accuracy of 96.89%, 95.23%, and 97.22%, respectively. We used expert radiologists' hand-curated segmentation of GGO as ground truth for quantificational performance analysis. Our research results indicate that this algorithm performs well found in the literature. © 2023 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.

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 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.

IoT-enabled stacked ensemble of deep neural networks for the diagnosis of COVID-19 using chest CT scans

The ongoing COVID-19 (novel coronavirus disease 2019) pandemic has triggered a global emergency, resulting in significant casualties and a negative effect on socioeconomic and healthcare systems around the world. Hence, automatic and fast screening of COVID-19 infections has become an urgent need of this pandemic. Real-time reverse transcription polymerase chain reaction (RT-PCR), a commonly used primary clinical method, is expensive and time-consuming for skilled health professionals. With the aid of various AI functionalities and advanced technologies, chest CT scans may thus be a viable alternative for quick and automatic screening of COVID-19. At the moment, significant advances in 5G cellular and internet of things (IoT) technology are finding use in various applications in the healthcare sector. This study presents an IoT-enabled deep learning-based stacking model to analyze chest CT scans for effective diagnosis of COVID-19 encounters. At first, patient data will be obtained using IoT devices and sent to a cloud server during the data procurement stage. Then we use different fine-tuned CNN sub-models, which are stacked together using a meta-learner to detect COVID-19 infection from input CT scans. The proposed model is evaluated using an open access dataset containing both COVID-19 infected and non-COVID CT images. Evaluation results show the efficacy of the proposed stacked model containing fine-tuned CNNs and a meta-learner in detecting coronavirus infections using CT scans. [ABSTRACT FROM AUTHOR] Copyright of Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

LiMS-Net: A Lightweight Multi-Scale CNN for COVID-19 Detection from Chest CT Scans

Recent years have witnessed a rise in employing deep learning methods, especially convolutional neural networks (CNNs) for detection of COVID-19 cases using chest CT scans. Most of the state-of-the-art models demand a huge amount of parameters which often suffer from overfitting in the presence of limited training samples such as chest CT data and thereby, reducing the detection performance. To handle these issues, in this paper, a lightweight multi-scale CNN called LiMS-Net is proposed. The LiMS-Net contains two feature learning blocks where, in each block, filters of different sizes are applied in parallel to derive multi-scale features from the suspicious regions and an additional filter is subsequently employed to capture discriminant features. The model has only 2.53M parameters and therefore, requires low computational cost and memory space when compared to pretrained CNN architectures. Comprehensive experiments are carried out using a publicly available COVID-19 CT dataset and the results demonstrate that the proposed model achieves higher performance than many pretrained CNN models and state-of-the-art methods even in the presence of limited CT data. Our model achieves an accuracy of 92.11% and an F1-score of 92.59% for detection of COVID-19 from CT scans. Further, the results on a relatively larger CT dataset indicate the effectiveness of the proposed model. © 2023 Association for Computing Machinery.

Covid 19 Prediction Through Chest CT Scans using Deep Learning and Deploying Model on Flask Web

Given the infection's wide growth, one of the biggest challenges on the planet right now is identifying Corona Virus Disease 2019 (COVID-19). Recent findings show that, with over 225M confirmed instances, the number of people who have been diagnosed with COVID-19 is drastically increasing;Around the world, the sickness is affecting several countries. In this study, the global COVID-19 circulation incidence is briefly examined, and a deep convolutional neural network (CNN) artificial intelligence model is developed to identify COVID19 patients using real-world information. To find such patients, the model looks at chest CT scan images. The results show that such an approach is helpful in diagnosing COVID-19 since CT scans are easily accessible fast and inexpensively. This suggested approach is effective at detecting COVID-19 and achieves an F-measure range of 95-99%, according to empirical findings from 100 CT scan pictures of actual patients. The suggested model has a considerable impact in identifying sick individuals. © 2022 IEEE.

A Deep Learning-based 3D CNN for Automated COVID-19 Lung Lesions Segmentation from 3D Chest CT Scans

This paper presents an automated COVID-19 lung lesions segmentation method based on a deep three-dimensional convolutional neural network model which automatically detects and extracts multifocal, bilateral and peripheral lung lesions from chest 3D-CT scans. The proposed CNN model is based on a modified 11-layer U-net architecture and employs a loss function that combines Dice coefficient and Cross-Entropy. It has been tested and evaluated on Covid-19-20-v2 training dataset containing a total of 199 3D-CT scans of different subjects with COVID-19 lesions representing different sizes, shapes and locations in CT images. The obtained results have proven to be satisfactory and objective, as well as similar and close to ground truth data provided by medical experts. On these challenging CT data, the proposed CNN obtained average scores of 0.7639, 0.8129 and 0.9986 corresponding to Dice Similarity Coefficient, Sensitivity and Specificity metrics respectively. © 2022 IEEE.

Image Enhancement Techniques on Chest X-Ray Images to Improve COVID-19 Detection

The COVID-19 pandemic has claimed many lives. The diagnosis is made to prevent the spread of COVID-19. One of the diagnostic methods that have now become the gold standard is RT-PCR, but this method still has shortcomings in terms of accuracy so it is at risk of causing inaccurate decision-making. The use of medical imaging techniques such as CXR and chest CT scans in the diagnosis of COVID-19 is considered to be able to increase the accuracy of COVID-19 detection so that the risk of making inappropriate decisions can be minimized. Compared to a chest CT scan, CXR is considered superior in terms of price and availability so with these advantages the use of CXR is more effective in diagnosing COVID-19. However, it should be noted that in terms of performance, the chest CT scan far outperformed CXR. For CXR to be better utilized, image enhancement techniques are applied and combined with several classification algorithms. The experiments on two datasets showed that applying BCET (Balance Contrast Enhancement Technique) prior to classifying consistently outperforms other classification methods without enhancement techniques on other compared methods. Moreover, the SVM algorithm achieved the best classification results for all image types in both datasets by scoring the highest AUC compared to other algorithms. © 2022 IEEE.

Vision Transformer Based COVID-19 Detection Using Chest CT-scan images

The fast proliferation of the coronavirus around the globe has put several countries' healthcare systems in danger of collapsing. As a result, locating and separating COVID-19-positive patients is a critical task. Deep Learning approaches were used in several computer-aided automated systems that utilized chest computed tomography (CT-scan) or X-ray images to create diagnostic tools. However, current Convolutional Neural Network (CNN) based approaches cannot capture the global context because of inherent image-specific inductive bias. These techniques also require large and labeled datasets to train the algorithm, but not many labeled COVID-19 datasets exist publicly. To mitigate the problem, we have developed a self-attention-based Vision Transformer (ViT) architecture using CT-scan. The proposed ViT model achieves an accuracy of 98.39% on the popular SARS-CoV-2 datasets, outperforming the existing state-of-the-art CNN-based models by 1%. We also provide the characteristics of CT scan images of the COVID-19-affected patients and an error analysis of the model's outcome. Our findings show that the proposed ViT-based model can be an alternative option for medical professionals for effective COVID-19 screening. The implementation details of the proposed model can be accessed at https://github.com/Pranabiitp/ViT. © 2022 IEEE.

Convolutional Neural Network (CNN) in COVID-19 Detection: A Case Study with Chest CT Scan Images

Deep Learning, especially Convolutional Neural Net-works (CNN) have been performing very well for the last decade in medical image classification. CNN has already shown a great prospect in detecting COVID-19 from chest X-ray images. However, due to its three dimensional data, chest CT scan images can provide better understanding of the affected area through segmentation in comparison to the chest X-ray images. But the chest CT scan images have not been explored enough to achieve sufficiently good results in comparison to the X-ray images. However, with proper image pre-processing, fine tuning, and optimization of the models better results can be achieved. This work aims in contributing to filling this void in the literature. On this aspect, this work explores and designs both custom CNN model and three other models based on transfer learning: InceptionV3, ResNet50, and VGG19. The best performing model is VGG19 with an accuracy of 98.39% and F-1 score of 98.52%. The main contribution of this work includes: (i) modeling a custom CNN model and three pre-trained models based on InceptionV3, ResNet50, and VGG19 (ii) training and validating the models with a comparatively larger dataset of 1252 COVID-19 and 1230 non-COVID CT images (iii) fine tune and optimize the designed models based on the parameters like number of dense layers, optimizer, learning rate, batch size, decay rate, and activation functions to achieve better results than the most of the state-of-the-art literature (iv) the designed models are made public in [1] for reproducibility by the research community for further developments and improvements. © 2022 IEEE.

Transfer Learning based on Covid-19 Classification using CT Scan Image

Initially, the coronavirus infection has been diagnosed by using the Chest CT scan and x-ray images of the patients. An accurate representation of the victim's respiratory system allows the medical practitioners to detect the covid-19 infection. The first step of the proposed approach is to preprocess the image in order to eliminate any undesirable noise that may be present in medical images. Following that, the intended features are retrieved from a processed image. Finally, Transfer Learning is used to categorize the data. The CT scan based representations are separated by using a U-net simulation, and the split representation is then used to train and analyze the data by using the v3 simulator, which helps to differentiate the coronavirus infection and pneumonia infection and securely protect the resulting documents. © 2022 IEEE.

Res-Dense Net for 3D Covid Chest CT-Scan Classification

One of the most contentious areas of research in Medical Image Preprocessing is 3D CT-scan. With the rapid spread of COVID-19, the function of CT-scan in properly and swiftly diagnosing the disease has become critical. It has a positive impact on infection prevention. There are many tasks to diagnose the illness through CT-scan images, include COVID-19. In this paper, we propose a method that using a Stacking Deep Neural Network to detect the Covid 19 through the series of 3D CT-scans images. In our method, we experiment with two backbones are DenseNet 121 and ResNet 101. This method achieves a competitive performance on some evaluation metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19 diagnosis using chest CT scans and deep convolutional neural networks evolved by IP-based sine-cosine algorithm.

The prevalence of the COVID-19 virus and its variants has influenced all aspects of our life, and therefore, the precise diagnosis of this disease is vital. If a polymerase chain reaction test for a subject is negative, but he/she cannot easily breathe, taking a computed tomography (CT) image from his/her lung is urgently recommended. This study aims to optimize a deep convolution neural network (DCNN) structure to increase the COVID-19 diagnosis accuracy in lung CT images. This paper employs the sine-cosine algorithm (SCA) to optimize the structure of DCNN to take raw CT images and determine their status. Three improvements based on regular SCA are proposed to enhance both the accuracy and speed of the results. First, a new encoding approach is proposed based on the internet protocol (IP) address. Then, an enfeebled layer is proposed to generate a variable-length DCNN. The suggested model is examined over the COVID-CT and SARS-CoV-2 datasets. The proposed method is compared to a standard DCNN and seven variable-length models in terms of five known metrics, including sensitivity, accuracy, specificity, F1-score, precision, and receiver operative curve (ROC) and precision-recall curves. The results demonstrate that the proposed DCNN-IPSCA surpasses other benchmarks, achieving final accuracy of (98.32% and 98.01%), the sensitivity of (97.22% and 96.23%), and specificity of (96.77% and 96.44%) on the SARS-CoV-2 and COVID-CT datasets, respectively. Also, the proposed DCNN-IPSCA performs much better than the standard DCNN, with GPU and CPU training times, which are 387.69 and 63.10 times faster, respectively.

A Large Imaging Database and Novel Deep Neural Architecture for Covid-19 Diagnosis

Deep learning methodologies constitute nowadays the main approach for medical image analysis and disease prediction. Large annotated databases are necessary for developing these methodologies;such databases are difficult to obtain and to make publicly available for use by researchers and medical experts. In this paper, we focus on diagnosis of Covid-19 based on chest 3-D CT scans and develop a dual knowledge framework, including a large imaging database and a novel deep neural architecture. We introduce COV19-CT-DB, a very large database annotated for COVID-19 that consists of 7,750 3-D CT scans, 1,650 of which refer to COVID-19 cases and 6,100 to non-COVID19 cases. We use this database to train and develop the RACNet architecture. This architecture performs 3-D analysis based on a CNN-RNN network and handles input CT scans of different lengths, through the introduction of dynamic routing, feature alignment and a mask layer. We conduct a large experimental study that illustrates that the RACNet network has the best performance compared to other deep neural networks i) when trained and tested on COV19-CT-DB;ii) when tested, or when applied, through transfer learning, to other public databases. © 2022 IEEE.

Patient gender identity information in Covid-19 severity detection on 3D chest CT scans via deep learning

Covid-19 has infected a large number of people the last two years. Covid-19 affects the lungs and may have disastrous consequences on human health if not taken care of immediately. This paper aims to investigate if we can distinguish between Covid-19 positive non-severe and severe cases using a deep learning model given the patient gender identity information is included in the classification. We use a 3D chest CT dataset of 50 patients and slice, preprocess and convert them to 2D images. During the analysis, we conduct different scenarios to understand how the patent gender identity information affects the deep learning classification. The results show that Covid-19 severity can be distinguished using a ResNet50 deep learning architecture in the CT scans of male and female patients with high accuracy. However, the accuracy drops when no gender identity information is involved in the classification. © 2022 IEEE.

Evolving deep convolutional neural networks by IP-based marine predator algorithm for COVID-19 diagnosis using chest CT scans.

This paper proposes an optimal structured deep convolutional neural network (DCNN) based on the marine predator algorithm (MPA) to construct a novel automatic diagnosis platform that may help radiologists identify COVID-19 and non-COVID-19 patients based on CT scan categorization and analysis. The goal is met with the help of three modifications based on the regular MPA. First, a novel encoding scheme based on Internet Protocol (IP) addresses is proposed, followed by introducing an Enfeebled layer to build a variable-length DCNN. Finally, the learning process divides big datasets into smaller chunks that are randomly evaluated. The proposed model is compared to the COVID-CT and SARS-CoV-2 datasets to undertake a complete evaluation. Following that, the performance of the developed model (DCNN-IPMPA) is compared to that of a typical DCNN and seven variable-length models using five well-known comparison metrics, as well as the receiver operating characteristic and precision-recall curves. The results show that the DCNN-IPMPA outperforms other benchmarks, with a final accuracy of 97.21% on the SARS-CoV-2 dataset and 97.94% on the COVID-CT dataset. Also, timing analysis indicates that the DCNN processing time is the best among all benchmarks as expected; however, DCNN-IPMPA represents a competitive result compared to the standard DCNN.

A Survey on Deep Learning and Machine Learning for COVID-19 Detection

Coronaviruses are a type of virus that can cause a variety of disorders and exist in different types. COVID-19 is derived from a special type of a respiratory illness caused by the SARS-CoV-2 virus, discovered in 2019. Approximately two years ago, COVID-19 was discovered in the Chinese city of Wuhan, and it has since become a worldwide source of concern. A COVID-19 confirmed patient is experiencing symptoms such as fever, fatigue, and a dry cough. Based on the results of laboratory tests and/or chest X-rays, the COVID-19 diagnosis is established. When it comes to research using chest CT scans /X-ray for the diagnosis of COVID-19, which is based on medical imaging, Artificial Intelligence (AI) approaches are increasingly being applied in a variety of ways. Machine learning and deep learning are fields of artificial intelligence that can be used to analyze the data that was acquired in order to better understand the origins of COVID-19. The outcomes of applying such an approach will aid in a better understanding of the nature of the threat and how it might be mitigated. For this reason, this work gives an overview of deep learning and machine learning approaches for the detection of COVID-19. Several COVID-19 detection methods are discussed in detail, as well as the issues, current challenges associated with artificial intelligence and medical researchers' approaches to providing a comprehensive assessment of detecting COVID-19. © 2021 ACM.

Automatic classification between COVID-19 pneumonia, lung cancer and normal lung tissues on chest CT Scans

Coronavirus sickness (COVID-19) may be a pandemic sickness, that has already caused thousands of casualties and infected many countless individuals worldwide. Whereas most of the individuals infected with the COVID-19 intimate with delicate to moderate respiratory disease, some developed deadly respiratory illness. Any technological tool sanctioning screening of the COVID-19 infection with high accuracy will be crucially useful to the attention professionals. The usage of chest CT scan pictures for classifying and diagnosing COVID-19 respiratory illness has shown an excellent range of exactness and accuracy quite the other tool that lessens the number of deaths within the severe cases. This paper presents a proposed model of convolutional neural network (CNN) with a large multi-national dataset that is able to classify covid-19 pneumonia;lung cancer and the normal lung tissues from chest computed tomography (CT) scans with a classification accuracy of 94.05%.