Deep Learning Model to Quantify Left Atrium Volume on Routine Non-Contrast Chest Ct and Predict Adverse Outcomes

2021 ◽  
Author(s):  
Gilberto J. Aquino ◽  
Jordan Chamberlin ◽  
Megan Mercer ◽  
Madison Kocher ◽  
Ismail Kabakus ◽  
...  
Keyword(s):  
Deep Learning ◽  
Left Atrium ◽  
Learning Model ◽  
Chest Ct ◽  
Adverse Outcomes ◽  
Left Atrium Volume ◽  
Deep Learning Model

Explainable Deep Learning Model for COVID-19 Screening in Chest CT Images

2021 ◽  
pp. 981-990
Author(s):  
Mostafa El Habib Daho ◽  
Amin Khouani ◽  
Mohammed El Amine Lazouni ◽  
Sidi Ahmed Mahmoudi
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Ct Images ◽  
Chest Ct ◽  
Deep Learning Model

scholarly journals Quantitative Assessment of Chest CT Patterns in COVID-19 and Bacterial Pneumonia patients: A Deep Learning Perspective

2020 ◽  
Author(s):  
Myeongkyun Kang ◽  
Philip Chikontwe ◽  
Miguel Luna ◽  
Kyung Soo Hong ◽  
Jong Geol Jang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Bacterial Pneumonia ◽  
Clinical Information ◽  
Learning Model ◽  
Ct Images ◽  
Chest Ct ◽  
University Hospital ◽  
Severity Classification ◽  
Ct Analysis ◽  
Deep Learning Model

ABSTRACTAs the number of COVID-19 patients has increased worldwide, many efforts have been made to find common patterns in CT images of COVID-19 patients and to confirm the relevance of these patterns against other clinical information. The aim of this paper is to propose a new method that allowed us to find patterns which observed on CTs of patients, and further we use these patterns for disease and severity diagnosis. For the experiment, we performed a retrospective cohort study of 170 confirmed patients with COVID-19 and bacterial pneumonia acquired at Yeungnam University hospital in Daegu, Korea. We extracted lesions inside the lungs from the CT images and classified whether these lesions were from COVID-19 patients or bacterial pneumonia patients by applying a deep learning model. From our experiments, we found 20 patterns that have a major effect on the classification performance of the deep learning model. Crazy-paving was extracted as a major pattern of bacterial pneumonia, while Ground-glass opacities (GGOs) in the peripheral lungs as that of COVID-19. Diffuse GGOs in the central and peripheral lungs was considered to be a key factor for severity classification. The proposed method achieved an accuracy of 91.2% for classifying COVID-19 and bacterial pneumonia with 95% reported for severity classification. Chest CT analysis with constructed lesion clusters revealed well-known COVID-19 CT manifestations comparable to manual CT analysis. Moreover, the constructed patient level histogram with/without radiomics features showed feasibility and improved accuracy for both disease and severity classification with key clinical implications.

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 COVID-19 Pneumonia Severity Grading: Test of A Trained Deep Learning Model

2020 ◽  
Author(s):  
Ying Zhang ◽  
Huawei Wu ◽  
Haitao Song ◽  
Xiaoqian Li ◽  
Shiteng Suo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Chest Ct ◽  
Characteristic Analysis ◽  
Lung Lobe ◽  
Manual Method ◽  
Good Ability ◽  
Severity Grading ◽  
Deep Learning Model ◽  
Lung Lobes

Abstract Purpose: To detect affected lung lobes and conduct severity grading of coronavirus disease 2019 (COVID-19) pneumonia on chest CT images using an artificial intelligence (AI) technique.Materials and Methods: We used a deep learning model which was previously developed and trained to extract visual features from chest CT exams for the detection and severity grading of COVID-19 pneumonia. In this retrospective study, we tested this model with COVID-19 pneumonia cases in our institution. The numbers of affected lung lobes and severity grading values were compared for the AI method and manual method via the paired Chi-square test. The severity grading capability of the AI method was evaluated using receiver operating characteristic analysis.Results: A total of 24 cases of confirmed COVID-19 were included (13 men and 11 women). The most frequent CT observation was bilateral ground-glass opacities with consolidation and more than one affected lung lobe. Most cases were mild cases. Compared with the manual method, the AI method presented excellent sensitivity (97.2%) and accuracy (80.8%) but poor specificity (57.1%) in detecting affected lung lobes and good ability (area under the curve=0.795, accuracy=91.6%) in severity grading of COVID-19. Additionally, the time consumed in checking the accuracy of the AI detected lesions within the whole lung was significantly shorter than that of severity assessment by the manual method (t=9.434, p<0.001).Conclusion: The AI method with our model is useful in evaluating the severity grading of COVID-19 pneumonia.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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