scholarly journals AI-based multi-modal integration (ScanCov scores) of clinical characteristics, lab tests and chest CTs improves COVID-19 outcome prediction of hospitalized patients

2020 ◽  
Author(s):  
Nathalie Lassau ◽  
Samy Ammari ◽  
Emilie Chouzenoux ◽  
Hugo Gortais ◽  
Paul Herent ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
Chest Ct ◽  
Biological Data ◽  
Learning Models ◽  
Prognostic Information ◽  
Radiology Reports ◽  
Early Predictors ◽  
Scan Data ◽  
Chest Ct Scan

The SARS-COV-2 pandemic has put pressure on Intensive Care Units, and made the identification of early predictors of disease severity a priority. We collected clinical, biological, chest CT scan data, and radiology reports from 1,003 coronavirus-infected patients from two French hospitals. Among 58 variables measured at admission, 11 clinical and 3 radiological variables were associated with severity. Next, using 506,341 chest CT images, we trained and evaluated deep learning models to segment the scans and reproduce radiologists’ annotations. We also built CT image-based deep learning models that predicted severity better than models based on the radiologists’ reports. Finally, we showed that adding CT scan information—either through radiologist lesion quantification or through deep learning—to clinical and biological data, improves prediction of severity. These findings show that CT scans contain novel and unique prognostic information, which we included in a 6-variable ScanCov severity score.

scholarly journals A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images

2021 ◽  
pp. 105014
Author(s):  
Khabir Uddin Ahamed ◽  
Manowarul Islam ◽  
Ashraf Uddin ◽  
Arnisha Akhter ◽  
Bikash Kumar Paul ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
Chest Ct ◽  
Learning Approach ◽  
X Ray ◽  
Chest Ct Scan

scholarly journals ResCovNet: A Deep Learning-Based Architecture For COVID-19 Detection From Chest CT Scan Images

2020 ◽  
Author(s):  
Ankan Ghosh Dastider ◽  
Mohseu Rashid Subah ◽  
Farhan Sadik ◽  
Tanvir Mahmud ◽  
Shaikh Anowarul Fattah
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
Chest Ct ◽  
Chest Ct Scan

Intelligent classification of lung malignancies using deep learning techniques

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Priyanka Yadlapalli ◽  
D. Bhavana ◽  
Suryanarayana Gunnam
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Ct Scan ◽  
Transfer Learning ◽  
Confusion Matrix ◽  
Chest Ct ◽  
Lung Nodules ◽  
Content Type ◽  
Chest Ct Scan

PurposeComputed tomography (CT) scan can provide valuable information in the diagnosis of lung diseases. To detect the location of the cancerous lung nodules, this work uses novel deep learning methods. The majority of the early investigations used CT, magnetic resonance and mammography imaging. Using appropriate procedures, the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung cancer. All of the methods used to discover and detect cancer illnesses are time-consuming, expensive and stressful for the patients. To address all of these issues, appropriate deep learning approaches for analyzing these medical images, which included CT scan images, were utilized.Design/methodology/approachRadiologists currently employ chest CT scans to detect lung cancer at an early stage. In certain situations, radiologists' perception plays a critical role in identifying lung melanoma which is incorrectly detected. Deep learning is a new, capable and influential approach for predicting medical images. In this paper, the authors employed deep transfer learning algorithms for intelligent classification of lung nodules. Convolutional neural networks (VGG16, VGG19, MobileNet and DenseNet169) are used to constrain the input and output layers of a chest CT scan image dataset.FindingsThe collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer, squamous and adenocarcinoma impacted chest CT scan images. According to the confusion matrix results, the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy, followed by VGG19 with 89.39%, MobileNet with 85.60% and DenseNet169 with 83.71% accuracy, which is analyzed using Google Collaborator.Originality/valueThe proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19, MobileNet and DenseNet169. The results are validated by computing the confusion matrix for each network type.

scholarly journals Object or Background: An Interpretable Deep Learning Model for Covid-19 Detection from CT-Scan Images

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1732
Author(s):  
Gurmail Singh ◽  
Kin-Choong Yow
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
Learning Model ◽  
Chest Ct ◽  
High Stake ◽  
Reasoning Process ◽  
Economic Problems ◽  
Learning Techniques ◽  
Deep Learning Model ◽  
Chest Ct Scan

The new strains of the pandemic Covid-19 are still looming. It is important to develop multiple approaches for timely and accurate detection of Covid-19 and its variants. Deep learning techniques are well proved for their efficiency in providing solutions to many social and economic problems. However, the transparency of the reasoning process of a deep learning model related to a high stake decision is a necessity. In this work, we propose an interpretable deep learning model Ps-ProtoPNet to detect Covid-19 from the medical images. Ps-ProtoPNet classifies the images by recognizing the objects rather than their background in the images. We demonstrate our model on the dataset of the chest CT-scan images. The highest accuracy that our model achieves is 99.29%

scholarly journals ai-corona: Radiologist-Assistant Deep Learning Framework for COVID-19 Diagnosis in Chest CT Scans

2020 ◽  
Author(s):  
M. Yousefzadeh ◽  
P. Esfahanian ◽  
S. M. S. Movahed ◽  
S. Gorgin ◽  
R. Lashgari ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
Chest Ct ◽  
The Other ◽  
Ct Scans ◽  
Lower Sensitivity ◽  
Imaging Features ◽  
Learning Framework ◽  
Experienced Radiologist ◽  
Chest Ct Scan

AbstractBackgroundWith the global outbreak of COVID-19 epidemic since early 2020, there has been considerable attention on CT-based diagnosis as an effective and reliable method. Recently, the advent of deep learning in medical diagnosis has been well proven. Convolutional Neural Networks (CNN) can be used to detect the COVID-19 infection imaging features in a chest CT scan. We introduce ai-corona, a radiologist-assistant deep learning framework for COVID-19 infection diagnosis using the chest CT scans.MethodOur dataset comprises 2121 cases of axial spiral chest CT scans in three classes; COVID-19 abnormal, non COVID-19 abnormal, and normal, from which 1764 cases were used for training and 357 cases for validation. The training set was annotated using the reports of two experienced radiologists. The COVID-19 abnormal class validation set was annotated using the general consensus of a collective of criteria that indicate COVID-19 infection. Moreover, the validation sets for the non COVID-19 abnormal and the normal classes were annotated by a different experienced radiologist. ai-corona constitutes a CNN-based feature extractor conjoined with an average pooling and a fully-connected layer to classify a given chest CT scan into the three aforementioned classes.ResultsWe compare the diagnosis performance of ai-corona, radiologists, and model-assisted radiologists for six combinations of distinguishing between the three mentioned classes, including COVID-19 abnormal vs. others, COVID-19 abnormal vs. normal, COVID-19 abnormal vs. non COVID-19 abnormal, non COVID-19 abnormal vs. others, normal vs. others, and normal vs. abnormal. ai-corona achieves an AUC score of 0.989 (95% CI: 0.984, 0.994), 0.997 (95% CI: 0.995, 0.999), 0.986 (95% CI: 0.981, 0.991), 0.959 (95% CI: 0.944, 0.974), 0.978 (95% CI: 0.968, 0.988), and 0.961 (95% CI: 0.951, 0.971) in each combination, respectively. By employing Bayesian statistics to calculate the accuracies at a 95% confidence interval, ai-corona surpasses the radiologists in distinguishing between the COVID-19 abnormal class and the other two classes (especially the non COVID-19 abnormal class). Our results show that radiologists’ diagnosis performance improves when incorporating ai-corona’s prediction. In addition, we also show that RT-PCR’s diagnosis has a much lower sensitivity compared to all the other methods.Conclusionai-corona is a radiologist-assistant deep learning framework for fast and accurate COVID-19 diagnosis in chest CT scans. Our results ascertain that our framework, as a reliable detection tool, also improves experts’ diagnosis performance and helps especially in diagnosing non-typical COVID-19 cases or non COVID-19 abnormal cases that manifest COVID-19 imaging features in chest CT scan. Our framework is available at: ai-corona.com

scholarly journals Deep learning based diagnosis of COVID-19 using chest CT-scan images

2020 ◽  
Author(s):  
Talha Anwar ◽  
Seemab Zakir
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
Model Performance ◽  
Chest Ct ◽  
Learning Rate ◽  
Diagnostic Purpose ◽  
Learning Technology ◽  
X Rays ◽  
Infected People ◽  
Chest Ct Scan

The Coronavirus disease (COVID-19) is an infectious disease that primarily affects lungs. This virus has spread in almost every continent. Countries are racing to slow down the spread by testing and treating patients. To diagnose the infected people, reverse transcription-polymerase chain reaction (RT-PCR) test is used. Because of colossal demand; PCR kits are under shortage, and to overcome this; radiographic techniques such as X-rays and CT-scan can be used for diagnostic purpose. In this paper, deep learning technology is used to diagnose COVID-19 in subjects through chest CT-scan. EfficientNet deep learning architecture is used for timely and accurate detection of coronavirus with an accuracy 0.897, F1 score 0.896, and AUC 0.895. Three different learning rate strategies are used, such as reducing the learning rate when model performance stops increasing (reduce on plateau), cyclic learning rate, and constant learning rate. Reduce on plateau strategy achieved F1-score of 0.9, cyclic learning rate and constant learning rate resulted in F1-score of 0.86 and 0.82, respectively. Implementation is available at github.com/talhaanwarch/Corona\_Virus/tree/master/CT\_scan

Sign in / Sign up

Export Citation Format

Share Document