Improved method for COVID-19 Classification of Complex-Architecture CNN from Chest CT volumes using Orthogonal Ensemble Networks

This paper introduces the improved method for the COVID-19 classification based on computed tomography (CT) volumes using a combination of a complex-architecture convolutional neural network (CNN) and orthogonal ensemble networks (OEN). The novel coronavirus disease reported in 2019 (COVID-19) is still spreading worldwide. Early and accurate diagnosis of COVID-19 is required in such a situation, and the CT scan is an essential examination. Various computer-aided diagnosis (CAD) methods have been developed to assist and accelerate doctors' diagnoses. Although one of the effective methods is ensemble learning, existing methods combine some major models which do not specialize in COVID-19. In this study, we attempted to improve the performance of a CNN for the COVID-19 classification based on chest CT volumes. The CNN model specializes in feature extraction from anisotropic chest CT volumes. We adopt the OEN, an ensemble learning method considering inter-model diversity, to boost its feature extraction ability. For the experiment, We used chest CT volumes of 1283 cases acquired in multiple medical institutions in Japan. The classification result on 257 test cases indicated that the combination could improve the classification performance. © 2023 SPIE.

Deep learning-based COVID-19 diagnosis using CT scans with laboratory and physiological parameters

The global economy has been dramatically impacted by COVID-19, which has spread to be a pandemic. COVID-19 virus affects the respiratory system, causing difficulty breathing in the patient. It is crucial to identify and treat infections as soon as possible. Traditional diagnostic reverse transcription-polymerase chain reaction (RT-PCR) methods require more time to find the infection. A high infection rate, slow laboratory analysis, and delayed test results caused the widespread and uncontrolled spread of the disease. This study aims to diagnose the COVID-19 epidemic by leveraging a modified convolutional neural network (CNN) to quickly and safely predict the disease's appearance from computed tomography (CT) scan images and a laboratory and physiological parameters dataset. A dataset representing 500 patients was used to train, test, and validate the CNN model with results in detecting COVID-19 having an accuracy, sensitivity, specificity, and F1-score of 99.33%, 99.09%, 99.52%, and 99.24%, respectively. These experimental results suggest that our strategy performs better than previously published approaches.

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.

The Compatibility of Chest CT Scan with RT-PCR in Suspected COVID-19 Patients

BACKGROUND: Thoracic computed tomography (CT) scan plays a role in detecting and assessing the progression of COVID-19. It can evaluate the response to the therapy given. In diagnosis, the CT scan of the chest may complement the limitations of reverse transcription polymerase chain reaction (RT-PCR). Several recent studies have discussed the importance of CT scans in COVID-19 patients with false-negative RT-PCR results. The sensitivity of chest CT scan in the diagnosis of COVID-19 is reportedly around 98%. AIM: This study aimed to determine the compatibility of CT scan of the thorax with RT-PCR in suspected COVID-19 patients. MATERIALS AND METHODS: This research was conducted in the Radiology Department of the Wahidin Sudirohusodo Hospital Makassar from April to December 2020 with 350 patients. The method used was a 2 x 2 table diagnostic test. RESULT(S): The study included 188 male patients (53.7%) and 162 female patients (46.2%). The most common age group was 46-65 years (35.4%). The most common types of lesions were ground-glass opacity (163 cases), consolidation (128 cases), and fibrosis (124 cases), mostly found in the inferior lobe with a predominantly peripheral or subpleural distribution. The sensitivity of the CT scan to the PCR examination was 86%, and the specificity was 91%. CONCLUSION(S): Thoracic CT scan was a good modality in establishing the diagnosis of COVID-19. CT scan of the chest with abnormalities could confirm the diagnosis in 88% of cases based on RT-PCR examination. It excluded the diagnosis in 91% based on the RT-PCR examination. The accuracy of the thoracic CT scan was 88% with RT-PCR as the reference value.Copyright © 2023 Sri Asriyani, Albert Alexander Alfonso, Mirna Muis, Andi Alfian Zainuddin, Irawaty Djaharuddin, Muhammad Ilyas.

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.

Comparison of pulmonary computed tomography scan findings and clinical symptoms of COVID-19 in three outbreak peaks in Isfahan, Iran.

Background: The aim of the present study was to investigate and compare the relationship between the anatomical distribution of pulmonary lesions in computed tomography scan of patients with COVID-19. Materials and Methods: This is a cross-sectional study that was performed in 2020-2021 in Isfahan on 300 patients infected with COVID-19 pneumonia. We collected data on the age, gender, and comorbidities of patients. In addition, we gathered data on the clinical manifestations of the patients from their medical records. Results: We noted a significant decline in symptoms such as fever and sputum production in the second and third peak in comparison to the first peak (P < 0.05). Moreover, cough and muscular pain were higher in the second and third peaks compared to the first peak (P < 0.05). Cough was the most common clinical manifestation related to the peripheral distribution of the involvements, bilateral lung disease, and right lower lobe (RLL) involvements in the first peak. In the second COVID-19 peak, fever and cough were the most common clinical findings, respectively, that were mostly associated with peripheral distribution and left lower lobe involvement. Conclusion: Cough was the most common clinical manifestation related to the peripheral distribution of the involvements, bilateral lung disease, and RLL involvements in the first peak. In the second COVID-19 peak, fever and cough were the most common clinical findings.

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.

Effect Of Termination Of Pregnancy On Pregnant Women With Covid

This descriptive analytic study was aimed to observe the clinical features and outcomes of pregnant women with COVID which has been confirmed through PCR or lung CT Scanning, and their pregnancy have been terminated because of mother's critical condition or obstetric indications, since the onset of COVID pandemic in Alzahra or EmamReza Hospitals in Tabriz. Before pregnancy termination, the enrolled mothers were categorized into four groups as mild, moderate, severe, and critical conditions, according to the percentage of lung's involvement in CT scan, SPO2 saturation, clinical symptoms, and delivery and paraclinical tests. These items were reassessed 24 and 48 hours after termination of pregnancy, and the changes of values were recorded and consequently compared through statistical methods in two phases of before and after delivery. The changes in the severity of disease during the measurement times (before termination of pregnancy, and 24 hours and 48 hours after delivery) were statistically significant (P < 0.0001). All 68 patients who were in the mild phase of the disease before delivery were discharged without any death. One case (2.8%) in moderate phase, 6(24%) in severe and 3(18.8%) in critical phase died. In conclusion, early termination of pregnancy in pregnancies complicated with COVID can improve the outcome. [ FROM AUTHOR] Copyright of Journal of Pharmaceutical Negative Results is the property of ResearchTrentz 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 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 . (Copyright applies to all s.)

A Survey on Diagnosing Pulmonary Diseases

Due to the increase in world population, a lot of research is being done in the medical sciences. Pandemics and epidemics have multiple outbreaks in many regions of the world. In order to solve the issue, creative probing is being used. Most of the illnesses in the group are obstructive and may result in a loss of life. Heart and lung conditions make up a large portion of the obstructive illnesses in this group. More than 5 lakh people die each year from lung illnesses, generally known as pulmonary disorders, with an equal proportion of men and women affected. Each disease has unique symptoms that are connected to it in the fields of medicine and healthcare. There are several new tests that are being developed to identify each of the dangerous diseases that are on the rise. This results from the necessity for quick illness prediction. This paper examines numerous studies and experiments carried out over a variety of timelines and approaches selected by various experiments, carefully examining the benefits and drawbacks of the approaches in order to construct an appropriate model for the cause. It focuses on the study of diagnosing pulmonary disorders and making the user's task easy in understanding the scanned images obtained. © 2023 IEEE.

Different Deep Learning Based Classification Models for COVID-19 CT-Scans and Lesion Segmentation Through the cGAN-UNet Hybrid Method

The new coronavirus, which emerged in early 2020, caused a major global health crisis in 7 continents. An essential step towards fighting this virus is computed tomography (CT) scans. CT scans are an effective radiological method to detecting the diagnosis in early stage, but have greatly increased the workload of radiologists. For this reason, there are systems needed that will reduce the duration of CT examinations and assist radiologists. In this study, a two-stage system has been proposed for COVID-19 detection. First, a hybrid method is proposed that can segment the infected region from CT images. The reason for this is that there is not always a reference image in the datasets used in the classification. For this purpose;UNet, UNet++, SegNet and PsPNet were used both separately and as hybrids with GAN, to automatically segment infected areas from chest CT slices. According to the segmentation results, cGAN-UNet hybrid system was selected as the most successful method. Experimental results show that the proposed method achieves a segmentation success with a dice score of 92.32% and IoU score of 86.41%. In the second stage, three classifiers which include a Convolutional Neural Network (CNN), a PatchCNN and a Capsule Neural Network (CapsNet) were used to classify the generated masks as either COVID-19 or not, using the segmented images obtained from cGAN-UNet. Success of these classifiers was 99.20%, 92.55% and 73.84%, respectively. According to these results, the highest success was achieved in the system where cGAN-Unet and CNN are used together. © 2023 Lavoisier. All rights reserved.

MWG-Net: Multi-scale Wavelet Guidance Network for COVID-19 Lung Infection Segmentation from CT Images

Recently, accurate segmentation of COVID-19 infection from computed tomography (CT) scans is critical for the diagnosis and treatment of COVID-19. However, infection segmentation is a challenging task due to various textures, sizes and locations of infections, low contrast, and blurred boundaries. To address these problems, we propose a novel Multi-scale Wavelet Guidance Network (MWG-Net) for COVID-19 lung infection by integrating the multi-scale information of wavelet domain into the encoder and decoder of the convolutional neural network (CNN). In particular, we propose the Wavelet Guidance Module (WGM) and Wavelet &Edge Guidance Module (WEGM). Among them, the WGM guides the encoder to extract infection details through the multi-scale spatial and frequency features in the wavelet domain, while the WEGM guides the decoder to recover infection details through the multi-scale wavelet representations and multi-scale infection edge information. Besides, a Progressive Fusion Module (PFM) is further developed to aggregate and explore multi-scale features of the encoder and decoder. Notably, we establish a COVID-19 segmentation dataset (named COVID-Seg-100) containing 5800+ annotated slices for performance evaluation. Furthermore, we conduct extensive experiments to compare our method with other state-of-the-art approaches on our COVID-19-Seg-100 and two publicly available datasets, i.e., MosMedData and COVID-SemiSeg. The results show that our MWG-Net outperforms state-of-the-art methods on different datasets and can achieve more accurate and promising COVID-19 lung infection segmentation. IEEE

Automated diagnosis of COVID stages using texture-based Gabor features in variational mode decomposition from CT images

COVID-19 is a deadly and fast-spreading disease that makes early death by affecting human organs, primarily the lungs. The detection of COVID in the early stages is crucial as it may help restrict the spread of the progress. The traditional and trending tools are manual, time-inefficient, and less accurate. Hence, an automated diagnosis of COVID is needed to detect COVID in the early stages. Recently, several methods for exploiting computed tomography (CT) scan pictures to detect COVID have been developed;however, none are effective in detecting COVID at the preliminary phase. We propose a method based on two-dimensional variational mode decomposition in this work. This proposed approach decomposes pre-processed CT scan pictures into sub-bands. The texture-based Gabor filter bank extracts the relevant features, and the student's t-value is used to recognize robust traits. After that, linear discriminative analysis (LDA) reduces the dimensionality of features and provides ranks for robust features. Only the first 14 LDA features are qualified for classification. Finally, the least square- support vector machine (SVM) (radial basis function) classifier distinguishes between COVID and non-COVID CT lung images. The results of the trial showed that our model outperformed cutting-edge methods for COVID classification. Using tenfold cross-validation, this model achieved an improved classification accuracy of 93.96%, a specificity of 95.59%, and an F1 score of 93%. To validate our proposed methodology, we conducted different relative experiments with deep learning and traditional machine learning-based models like random forest, K-nearest neighbor, SVM, convolutional neural network, and recurrent neural network. The proposed model is ready to help radiologists identify diseases daily. © 2023 Wiley Periodicals LLC.

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.

Computational Fluid Dynamics-Based Optimization of Face Mask Filter Media Using Micro-Computed Tomography Scans

During the COVID-19 pandemic, face masks have become an important protective measure for reducing the spread of potentially infectious aerosol particles emitted while speaking, coughing, or simply breathing. In this work, a voxel-based numerical model obtained from micro-computed tomography (microCT) scans of a medical mask was validated by comparing fractional filtration efficiency and net pressure loss to values measured at an in-house mask test bench after discharging the mask in isopropanol. Varying mean fiber diameter, solid volume fraction, and thickness of the filter medium, parametric studies based on a digital twin of the mask sample were carried out. It is demonstrated that face masks can be designed where filtration efficiency, pressure drop, and material consumption is improved compared to the base case. © 2023 The Authors. Chemical Engineering Technology published by Wiley-VCH GmbH.

VGG-FusionNet: A Feature Fusion Framework from CT scan and Chest X-ray Images based Deep Learning for COVID-19 Detection

COVID-19 has been rampant across the globe since it was discovered in 2020, but the method of virus detection still lacks efficiency and requires human resources. Given the slow delivery of the PCR test and the many possible false negatives of the rapid tests, medical imaging such as a chest computed tomography (CT) scan or chest X-ray (CXR) is an alternative and efficient way to detect the coronavirus accurately. For the past two years, many researchers have proposed different deep learning methods for COVID-19 detection using CT scans or CXR images. Due to the lack of available data, our study aims to propose a new deep learning framework VGG-FusionNet that takes advantage of integrating features from both CT scan and CXR images while avoiding some pitfalls from previous studies, including a high risk of bias due to lack of demographic information for the dataset, poor reproducibility, and no evaluation on different data sources to study the generalizability. Specifically, we use the convolutional layers of GoogLeNet, ResNet, and VGG to extract features from CT scan and CXR images and fuse them before training through fully connected layers. The result shows that using VGG's convolutional layers achieves the best overall performance with an accuracy of 0.93. Our proposed framework outperforms the deep learning models, using features from CT scans or CXR. © 2022 IEEE.

A Novel Automated Classification and Segmentation for COVID-19 using 3D CT Scans

Medical image classification and segmentation based on deep learning (DL) are emergency research topics for diagnosing variant viruses of the current COVID-19 situation. In COVID-19 computed tomography (CT) images of the lungs, ground glass turbidity is the most common finding that requires specialist diagnosis. Based on this situation, some researchers propose the relevant DL models which can replace professional diagnostic specialists in clinics when lacking expertise. However, although DL methods have a stunning performance in medical image processing, the limited datasets can be a challenge in developing the accuracy of diagnosis at the human level. In addition, deep learning algorithms face the challenge of classifying and segmenting medical images in three or even multiple dimensions and maintaining high accuracy rates. Consequently, with a guaranteed high level of accuracy, our model can classify the patients' CT images into three types: Normal, Pneumonia and COVID. Subsequently, two datasets are used for segmentation, one of the datasets even has only a limited amount of data (20 cases). Our system combined the classification model and the segmentation model together, a fully integrated diagnostic model was built on the basis of ResNet50 and 3D U-Net algorithm. By feeding with different datasets, the COVID image segmentation of the infected area will be carried out according to classification results. Our model achieves 94.52% accuracy in the classification of lung lesions by 3 types: COVID, Pneumonia and Normal. For 2 labels (ground truth, lung lesions) segmentation, the model gets 99.57% of accuracy, 0.2191 of train loss and 0.78 ± 0.03 of MeanDice±Std, while the 4 labels (ground truth, left lung, right lung, lung lesions) segmentation achieves 98.89% of accuracy, 0.1132 of train loss and 0.83 ± 0.13 of MeanDice±Std. For future medical use, embedding the model into the medical facilities might be an efficient way of assisting or substituting doctors with diagnoses, therefore, a broader range of the problem of variant viruses in the COVID-19 situation may also be successfully solved. © 2022 IEEE.

Using a 3D ResNet for Detecting the Presence and Severity of COVID-19 from CT Scans

Deep learning has been used to assist in the analysis of medical imaging. One use is the classification of Computed Tomography (CT) scans for detecting COVID-19 in subjects. This paper presents Cov3d, a three dimensional convolutional neural network for detecting the presence and severity of COVID-19 from chest CT scans. Trained on the COV19-CT-DB dataset with human expert annotations, it achieves a macro f1 score of 87.87 on the test set for the task of detecting the presence of COVID-19. This was the ‘runner-up' for this task in the ‘AI-enabled Medical Image Analysis Workshop and Covid-19 Diagnosis Competition' (MIA-COV19D). It achieved a macro f1 score of 46.00 for the task of classifying the severity of COVID-19 and was ranked in fourth place. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An Advanced Deep Learning Medical Image Recognition and Diagnosis of Respiratory System Viruses

Respiratory infections are a confusing and time-consuming task that caused recently a pandemic that affected the whole world. One of the pandemics was COVID-19 that has exposed the vulnerability of medical services across the world, particularly in underdeveloped nations. There comes a strong demand for developing new computer-assisted diagnosis tools to present cost-effective and rapid screening in locations wherein enormous traditional testing is impossible. Medical imaging becomes critical for diagnosing disease, X-rays and computed tomography (CT) scan are employed in the deep network which will be helpful in diagnosing diseases. This paper proposes a scanning model based on using a Mel Frequency Cepstral Coefficients (MFCC) features extracted from a respiratory virus CT-Scan image and then filtered by applying Gabor filter (GF). The filtered image is passed to Convolutional Neural Network (CNN) for classifying the image for the presence of a respiratory virus such as Covid, Viral Pneumonia or being a healthy normal image. The proposed system achieved a validation accuracy of 100% with an overall accuracy of 99.44%. © 2022 IEEE.

ADPT: An Automated Disease Prognosis Tool Towards Classifying Medical Disease Using Hybrid Architecture of Deep Learning Paradigm

The Covid 19 beta coronavirus, commonly known as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently one of the most significant RNA-type viruses in human health. However, more such epidemics occurred beforehand because they were not limited. Much research has recently been carried out on classifying the disease. Still, no automated diagnostic tools have been developed to identify multiple diseases using X-ray, Computed Tomography (CT) scan, or Magnetic Resonance Imaging (MRI) images. In this research, several Tate-of-the-art techniques have been applied to the Chest-Xray, CT scan, and MRI segmented images' datasets and trained them simultaneously. Deep learning models based on VGG16, VGG19, InceptionV3, ResNet50, Capsule Network, DenseNet architecture, Exception and Optimized Convolutional Neural Network (Optimized CNN) were applied to the detecting of Covid-19 contaminated situation, Alzheimer's disease, and Lung infected tissues. Due to efforts taken to reduce model losses and overfitting, the models' performances have improved in terms of accuracy. With the use of image augmentation techniques like flip-up, flip-down, flip-left, flip-right, etc., the size of the training dataset was further increased. In addition, we have proposed a mobile application by integrating a deep learning model to make the diagnosis faster. Eventually, we applied the Image fusion technique to analyze the medical images by extracting meaningful insights from the multimodal imaging modalities. © 2022 IEEE.