COV-CTX: A Deep Learning Approach to Detect COVID-19 from Lung CT and X-Ray Images (original) (raw)
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Automatic Detection of Coronavirus (COVID-19) from Chest CT Images using VGG16-Based Deep-Learning
2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME)
In recent months, coronavirus disease 2019 (COVID-19) has infected millions of people worldwide. In addition to the clinical tests like reverse transcriptionpolymerase chain reaction (RT-PCR), medical imaging techniques such as computed tomography (CT) can be used as a rapid technique to detect and evaluate patients infected by COVID-19. Conventionally, CT-based COVID-19 classification is done by a radiology expert. In this paper, we present a deep learning-based Convolutional Neural Network (CNN) model we developed for the classification of COVID-19 positive patients from healthy subjects using chest CT. We used 10979 chest CT images of 131 patients with COVID-19 and 150 healthy subjects for training, validating, and testing of the proposed model. Evaluation of the results showed the precision of 92%, sensitivity of 90%, specificity of 91%, F1-Score of 0.91, and accuracy of 90%. We have used the regions of infection segmented by a radiologist to increase the generalization and reliability of the results. The plotted heatmaps show that the developed model has focused only on the infected regions of the lungs by COVID-19 to make decisions.
Automated Covid-19 Detection System with CNN using Chest X-Ray and CT-Scans
Covid19 is the menace of this century. World Health Organization (WHO) declared it pandemic in February, 2020. This RNA virus has catastrophic impact of the entire human civilization since it was initially reported to have been erupted from Wuhan, a city in Hubei province of China in late December 2019. In the first wave millions of people died in many countries. Even the developed countries like USA, France, Italy, United Kingdom etc. were in shock and could not prevent loss of human lives with their well-established medical infrastructure. Strict lockdown, quarantines were imposed. The hospitals were outnumbered by the severely ill patients who needed ventilation support. Many died without treatment, dead bodies were on the streets and mass graves became a practice. Developing and underdeveloped countries faced even more disastrous situations. Since then the virus is mutating and giving new challenges to human society in developing a cure. Until now RTPCR and other test are carried out to detect the disease. But they take somewhat longer time. So researchers are using artificial intelligence based techniques especially deep learning methods to develop new models using the CT scans (CTS) and chest X-ray (CXR) images of the patients to detect the disease in real time. This work focuses on the methods developed so far for detecting Covid-19 using convolutional neural network and compare their performances.
A CNN Based Method for Detecting Covid-19 from CT Images
Computer Science, 2021
COVID-19 outbreak first emerged on December 31, 2019 in Wuhan, China. The Novel Coronavirus Disease is caused by the SAR-CoV-2 virus, which causes respiratory symptoms such as fever, cough, and shortness of breath. While scientists continue their fight against SARS-CoV-2 (2019-nCoV), one of the deadliest viruses in the last century, with tests to help diagnosis and prognosis, drug and vaccine discovery, Information Technologies mostly continues to work on early diagnosis, prognosis and prediction. The aim is to reveal systems with low margin of error that will alleviate the workload of healthcare professionals, as well as early diagnosis and initiation of treatment.Deep Learning and Computer vision is the most commonly used. Two class (covid, noncovid) classification solution, using the Artificial Intelligence Techniques, have been examined in this paper. CNN architecture, has been created to develop an model to disease detection process of COVID-19(2019-nCoV) virus infected patients from CT images consisting of NON-COVID and COVID classes. We have proposed the classifying of CT images using the 2 Convolutions and pool layers with the model which shortening the time for classification and achieved an accuracy of nearly 94.21%. Results show that the used model attains provide highly satisfying results and can be used for any image classification.
COVID-19 Detection with X-Ray and CT-Scan Using Machine Learning
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
We researched the diagnostic capabilities of deep learning on chest radiographs and an image classifier based on the COVID-Net was presented to classify chest MRI images. In the case of a small amount of COVID-19 data, data enhancement was proposed to expanded COVID-19 data 17 times. Our model aims at transfer learning, model integration and classify chest MRI images according to three labels: normal, COVID-19 and viral pneumonia. According to the accuracy and loss value, choose the models ResNet-101 and ResNet-152 with good effect for fusion, and dynamically improve their weight ratio during the training process. After training, the model can achieve 96.1% of the types of chest MRI images accuracy on the test set. This technology has higher sensitivity than radiologists in the screening and diagnosis of lung nodules. As an auxiliary diagnostic technology, it can help radiologists improve work efficiency and diagnostic accuracy. COVID-19 is posed as very infectious and deadly pneumonia type disease until recent time. Despite having lengthy testing time, RT-PCR is a proven testing methodology to detect corona virus infection. Sometimes, it might give more false positive and false negative results than the desired rates. Therefore, to assist the traditional RT-PCR methodology for accurate clinical diagnosis, COVID-19 screening can be adopted with X-Ray and CT scan images of lung of an individual. This image based diagnosis will bring radical change in detecting corona virus infection in human body with ease and having zero or near to zero false positives and false negatives rates. This paper reports a convolution neural network (CNN) based multi-image augmentation technique for detecting COVID-19 in chest X-Ray and chest CT scan images of corona virus suspected individuals. Multi-image augmentation makes use of discontinuity information obtained in the filtered images for increasing the number of effective examples for training the CNN model. With this approach, the proposed model exhibits higher classification accuracy around 95.38% and 98.97% for CT scan and X-Ray images respectively. CT scan images with multi-image augmentation chieves sensitivity of 94.78% and specificity of 95.98%, whereas X-Ray images with multi-image augmentation achieves sensitivity of 99.07% and specificity of 98.88%. Evaluation has been done on publicly available databases containing both chest X-Ray and CT scan images and the experimental results are also compared with ResNet-50 and VGG-16 models.
COVID-19 Detection from Chest X-Ray Images Using Deep Learning
CERN European Organization for Nuclear Research - Zenodo, 2022
COVID-19 is an important threat worldwide. This disease is caused by the novel SARS-CoV-2. CXR and CT images reveal speci c information about the disease. However, when interpreting these images, experiencing an overlap with other lung infections complicates the detection of the disease. Due to this situation, the need for computer-aided systems is increasing day by day. In this study, solutions were developed with proposed models based on deep neural networks (DNN). All the analyses were performed on a publicly available CXR dataset. This study offers a comparison of the deep learning models (SqueezeNet, Inception-V3, VGG16, MobileNet, Xception, VGG19+MobileNet (Concatenated)) that results in the detection and classi cation of a disease. Empirical evaluation demonstrates that the Inception-V3 model gives 90% accuracy with 100% precision for the COVID-19 infection. This model has been provided with better results compared to other models. In addition to the studies in the literature, it has been observed that the proposed pre-trained-based concatenated model gives very similar successful results to the other models.
Deep learning for COVID-19 diagnosis based on chest X-ray images
International Journal of Electrical and Computer Engineering (IJECE), 2021
Coronavirus disease 2019 (COVID-19) is a recent global pandemic that has affected many countries around the world, causing serious health problems, especially in the lungs. Although temperature testing is suggested as a firstline test for COVID-19, it was not reliable because many diseases have the same symptoms. Thus, we propose a deep learning method based on X-ray images that used a convolutional neural network (CNN) and transfer learning (TL) for COVID-19 diagnosis, and using gradient-weighted class activation mapping (Grad-CAM) technique for producing visual explanations for the COVID-19 infection area in the lung. The low sample size of coronavirus samples was considered a challenge, thus, this issue was overridden using data augmentation techniques. The study found that the proposed (CNN) and the modified pre-trained networks VGG16 and InceptionV3 achieved a promising result for COVID-19 diagnosis by using chest X-ray images. The proposed CNN was able to differentiate 284 patients with COVID-19 or normal with 98.2 percent for training accuracy and 96.66 percent for test accuracy and 100.0 percent sensitivity. The modified VGG16 achieved the best classification result between all with 100.0 percent for training accuracy and 98.33 percent for test accuracy and 100.0 percent sensitivity, but the proposed CNN overcame the others in the side of reducing the computational complexity and training time significantly.
Deep Learning-based COVID-19 Detection system using Pulmonary CT scans
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2021
One of the most significant pandemics has been raised in the form of Coronavirus disease 2019 (COVID-19). Many researchers have faced various types of challenges for finding the accurate model, which can automatically detect the COVID-19 using computed pulmonary tomography (CT) scans of the chest. This paper has also focused on the same area, and a fully automatic model has been developed, which can predict the COVID-19 using the chest CT scans. The performance of the proposed method has been evaluated by classifying the CT scans of community-acquired pneumonia (CAP) and other non-pneumonia. The proposed deep learning model is based on ResNet 50, named CORNet for the detection of COVID-19, and also performed the retrospective and multicenter analysis for the extraction of visual characteristics from volumetric chest CT scans during COVID-19 detection. Between August 2016 and May 2020, the datasets were obtained from six hospitals. Results are evaluated on the image dataset consisting of a total of 10,052 CT scan images generated from 7850 patients, and the average age of the patients was 50 years. The implemented model has achieved the sensitivity and specificity of 90% and 96%, per scanned image with an AUC of 0. 95.
Biomedical Signal Processing and Control
The Coronavirus (COVID-19) pandemic has created havoc on humanity by causing millions of deaths and adverse physical and mental health effects. To prepare humankind for the fast and efficient detection of the virus and its variants shortly, COVID-19 detection using Artificial Intelligence and Computer-Aided Diagnosis has been the subject of several studies. To detect COVID-19, there are numerous publicly accessible datasets of Chest X-rays that the researchers have combined to solve the problem of inadequate data. The cause for concern here is that in combining two or more datasets, some of the images might be duplicates, so a curated dataset has been used in this study, taken from an author's paper. This dataset consists of 1281 COVID-19, 3270 Normal X-rays, and 1656 viral-pneumonia infected Chest X-ray images. Dataset has been pre-processed and divided carefully to ensure that there are no duplicate images. A comparative study on many traditional pre-trained models was performed, analyzing top-performing models. Fine-tuned InceptionV3, Modified EfficientNet B0&B1 produced an accuracy of 99.78% on binary classification, i.e., covid-19 infected and normal Chest X-ray image. ResNetV2 had a classification accuracy of 97.90% for 3-class classification i.e., covid-19 infected, normal, and pneumonia. Furthermore, a trailblazing custom CNN-based model, CoviXNet, has been proposed consisting of 15 layers that take efficiency into account. The proposed model CoviXNet exhibited a 10-fold accuracy of 99.47% on binary classification and 96.61% on 3-class. CoviXNet has shown phenomenal performance with exceptional accuracy and minimum computational cost. We anticipate that this comparative study, along with the proposed model CoviXNet, can assist medical centers with the efficient real-life detection of Coronavirus.
Diagnosis of COVID-19 from chest X-ray images using Deep Learning Model
2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT), 2021
COVID-19 (Coronavirus-2019) is a disastrous pandemic which has affected the whole world damaging the whole ecosystem specially health. Researchers around the world have been contributing to the advancement in these conditions around the world. Medical practitioners have found that chest X-Ray images can used to detect whether a person suffers from covid-19 or not due to anomalies in chest radiography images. Continuing this motivation we have taken around 700 chest X ray images from different resources and applied two different transfer learning techniques to build a model which can detect the existence of covid-19 in a person. It uses VGG16 and Resnet50 deep learning models which utilize transfer learning to train their parameters. We have trained our both models for 50 epochs. VGG16 gives 76% of accuracy on test data and Resnet50 gives 85% accuracy on test data. We have tried to engineer thresholds for probability of classification thus changing specificity and sensitivity and also evaluated our models on various metrics such as classification report, confusion matrix heatmap, roc-auc score. This is by no means to be used for any medical procedures but can help other researchers to take some useful insights from it and carry forward the learning in building something which is production ready for contribution in our fight against COVID-19.
CNN Based Analysis of COVID-19 Using Chest X-Ray Images
2020
Coronavirus (COVID-19) is a deadly virus that originally originated from China's Wuhan district around November last year. It has a deadly effect on the human respiratory system if the condition escalates. Currently, millions of people have been affected worldwide, and in countries like India, the cases are still on the rise. Due to an increased rise in cases, the testing facilities are struggling to keep up with the demand for testing, and medical experts are looking for alternate ways to speed up testing. In this paper, we have experimented with one such way by developing a CNN-based model to classify the chest X-ray images for the detection of coronavirus affected cases. For result analysis, we have applied CNN based VGG 16, VGG 19, and custom model. Further, we compare the result of these models based on accuracy. In this experimental analysis, VGG 19 model detected 99% of COVID-19 infected cases accurately as compared to VGG 16. This is entirely an experimental study and should not be used in real-life scenarios without an evaluation by medical experts and determining the effectiveness of this method.