scholarly journals Evaluation of multiple open-source deep learning models for detecting and grading COVID-19 on chest radiographs

2021 ◽  
Vol 8 (06) ◽  
Author(s):  
Alexander Risman ◽  
Miguel Trelles ◽  
David W. Denning
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Chest Radiographs ◽  
Learning Models

Open-source deep learning models for acoustic detection and classification of orcas

2021 ◽  
Vol 150 (4) ◽  
pp. A286-A286
Author(s):  
Sadman Sakib ◽  
Steven Bergner ◽  
Dave Campbell ◽  
Mike Dowd ◽  
Fabio Frazao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Acoustic Detection ◽  
Learning Models

scholarly journals Pre-trained deep learning models in automatic COVID-19 diagnosis

2021 ◽  
Vol 22 (3) ◽  
pp. 1540
Author(s):  
Ahmed Wasif Reza ◽  
Md Mahamudul Hasan ◽  
Nazla Nowrin ◽  
Mir Moynuddin Ahmed Shibly
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Healthcare Professionals ◽  
Automated System ◽  
Primary Concern ◽  
X Rays ◽  
Learning Models ◽  
Efficient System ◽  
Empirical Results ◽  
History Of

Coronavirus Disease (COVID-19) is a devastating pandemic in the history of mankind. It is a highly contagious flu that can spread from human to human without revealing any symptoms. For being so contagious, detecting patients with it and isolating them has become the primary concern for healthcare professionals. This study presented an alternative way to identify COVID-19 patients by doing an automatic examination of chest X-rays of the patients. To develop such an efficient system, six pre-trained deep learning models were used. Those models were: VGG16, InceptionV3, Xception, DenseNet201, InceptionResNetV2, and EfficientNetB4. Those models were developed on two open-source datasets that have chest X-rays of patients diagnosed with COVID-19. Among the models, EfficientNetB4 achieved better performances on both datasets with 96% and 97% of accuracies. The empirical results were also exemplary. This type of automated system can help us fight this dangerous virus outbreak.

scholarly journals CReM: chemically reasonable mutations framework for structure generation

2020 ◽  
Author(s):  
Pavel Polishchuk
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
De Novo ◽  
De Novo Design ◽  
Learning Models ◽  
Structure Generation ◽  
Design Studies ◽  
Flexible Control

Abstract Structure generators are widely used in de novo design studies and their performance substantially influences an outcome. Approaches based on deep learning models and conventional atom-based approaches may result in invalid structures and did not address their synthetic feasibility issues. Conventional reaction-based approaches result in synthetically feasible compounds but novelty and diversity of generated compounds may be limited. Fragment-based approaches can provide better novelty and diversity of generated compounds but the issue of synthetic complexity of generated structure was not explicitly addressed before. Here, we developed a new fragment-based approach which results in chemically valid structures by design and gives flexible control over diversity, novelty, synthetic complexity and chemotypes of generated compounds. The approach was implemented as an open-source Python module.

scholarly journals Image and structured data analysis for prognostication of health outcomes in patients presenting to the Emergency Department during the COVID-19 pandemic

2021 ◽  
Author(s):  
Liam Butler ◽  
Ibrahim Karabayir ◽  
Mohammad Samie Tootooni ◽  
Majid Afshar ◽  
Ari Goldberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Intensive Care ◽  
Deep Learning ◽  
Clinical Data ◽  
Chest Radiographs ◽  
Gradient Boosting ◽  
Learning Models ◽  
Icu Admission ◽  
Light Gradient

Background: Patients admitted to the emergency department (ED) with COVID-19 symptoms are routinely required to have chest radiographs and computed tomography (CT) scans. COVID-19 infection has been directly related to development of acute respiratory distress syndrome (ARDS) and severe infections lead to admission to intensive care and can also lead to death. The use of clinical data in machine learning models available at time of admission to ED can be used to assess possible risk of ARDS, need for intensive care unit (ICU) admission as well as risk of mortality. In addition, chest radiographs can be inputted into a deep learning model to further assess these risks. Purpose: This research aimed to develop machine and deep learning models using both structured clinical data and image data from the electronic health record (EHR) to adverse outcomes following ED admission. Materials and Methods: Light Gradient Boosting Machines (LightGBM) was used as the main machine learning algorithm using all clinical data including 42 variables. Compact models were also developed using 15 the most important variables to increase applicability of the models in clinical settings. To predict risk of the aforementioned health outcome events, transfer learning from the CheXNet model was implemented on our data as well. This research utilized clinical data and chest radiographs of 3571 patients 18 years and older admitted to the emergency department between 9th March 2020 and 29th October 2020 at Loyola University Medical Center. Main Findings: Our research results show that we can detect COVID-19 infection (AUC = 0.790 (0.746-0.835)) and predict the risk of developing ARDS (AUC = 0.781 (0.690-0.872), ICU admission (AUC = 0.675 (0.620-0.713)), and mortality (AUC = 0.759 (0.678-0.840)) at moderate accuracy from both chest X-ray images and clinical data. Principal Conclusions: The results can help in clinical decision making, especially when addressing ARDS and mortality, during the assessment of patients admitted to the ED with or without COVID-19 symptoms.

Computer Aided Diagnosis of Pneumonia from Chest Radiographs

2019 ◽  
Vol 16 (10) ◽  
pp. 4202-4213
Author(s):  
Priyanka Malhotra ◽  
Sheifali Gupta ◽  
Deepika Koundal
Keyword(s):  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Chest Radiographs ◽  
Data Sets ◽  
Learning Models ◽  
Chest Disease ◽  
Computer Aided Analysis ◽  
Computer Aided ◽  
Chest X Ray ◽  
Aided Diagnosis

Pneumonia is a deadly chest disease and is a major culprit behind numerous deaths every year. Chest radiographs (CXR) are commonly used for quick and cheap diagnosis of chest diseases. The interpretation of CXR’s for diagnosing pneumonia is difficult. This has created an interest in computer-aided diagnosis (CAD) for CXR images. In this study, a brief review of literature based on computer aided analysis of chest radiograph images for identification of pneumonia using different machine learning and deep learning models is presented and a comparison of these different techniques has been provided. In addition, the study also presents various publicly available chest X-ray data sets for training, testing and validation of deep learning models.

scholarly journals IoT-based group size prediction and recommendation system using machine learning and deep learning techniques

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Deepti Chopra ◽  
Arvinder Kaur
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Open Source ◽  
Group Size ◽  
Open Source Software ◽  
Multilayer Perceptron ◽  
Recommendation System ◽  
Regression Trees ◽  
Learning Models ◽  
Information Foraging

AbstractIn an open source software development environment, it is hard to decide the number of group members required for resolving software issues. Developers generally reply to issues based totally on their domain knowledge and interest, and there are no predetermined groups. The developers openly collaborate on resolving the issues based on many factors, such as their interest, domain expertise, and availability. This study compares eight different algorithms employing machine learning and deep learning, namely—Convolutional Neural Network, Multilayer Perceptron, Classification and Regression Trees, Generalized Linear Model, Bayesian Additive Regression Trees, Gaussian Process, Random Forest and Conditional Inference Tree for predicting group size in five open source software projects developed and managed using an open source development framework GitHub. The social information foraging model has also been extended to predict group size in software issues, and its results compared to those obtained using machine learning and deep learning algorithms. The prediction results suggest that deep learning and machine learning models predict better than the extended social information foraging model, while the best-ranked model is a deep multilayer perceptron((R.M.S.E. sequelize—1.21, opencv—1.17, bitcoin—1.05, aseprite—1.01, electron—1.16). Also it was observed that issue labels helped improve the prediction performance of the machine learning and deep learning models. The prediction results of these models have been used to build an Issue Group Recommendation System as an Internet of Things application that recommends and alerts additional developers to help resolve an open issue.

scholarly journals Artificial intelligence–based detection of aortic stenosis from chest radiographs

2021 ◽  
Author(s):  
Daiju Ueda ◽  
Akira Yamamoto ◽  
Shoichi Ehara ◽  
Shinichi Iwata ◽  
Koji Abo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Aortic Stenosis ◽  
Predictive Value ◽  
Cost Effective ◽  
Chest Radiographs ◽  
Validation Dataset ◽  
Learning Models ◽  
Test Dataset ◽  
Sensitivity Specificity

Abstract Aims We aimed to develop models to detect aortic stenosis (AS) from chest radiographs—one of the most basic imaging tests—with artificial intelligence. Methods and Results We used 10433 retrospectively collected digital chest radiographs from 5638 patients to train, validate, and test three deep learning models. Chest radiographs were collected from patients who had also undergone echocardiography at a single institution between July 2016 and May 2019. These were labelled from the corresponding echocardiography assessments as AS-positive or AS-negative. The radiographs were separated on a patient basis into training (8327 images from 4512 patients, mean age 65 ± [SD] 15 years), validation (1041 images from 563 patients, mean age 65 ± 14 years), and test (1065 images from 563 patients, mean age 65 ± 14 years) datasets. The soft voting-based ensemble of the three developed models had the best overall performance for predicting AS with an AUC, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of 0.83 (95% CI 0.77–0.88), 0.78 (0.67–0.86), 0.71 (0.68–0.73), 0.71 (0.68–0.74), 0.18 (0.14–0.23), and 0.97 (0.96–0.98), respectively, in the validation dataset and 0.83 (0.78–0.88), 0.83 (0.74–0.90), 0.69 (0.66–0.72), 0.71 (0.68–0.73), 0.23 (0.19–0.28), and 0.97 (0.96–0.98), respectively, in the test dataset. Conclusion Deep learning models using chest radiographs have the potential to differentiate between radiographs of patients with and without AS. Lay summary We created AI models using deep learning to identify aortic stenosis from chest radiographs. Three AI models were developed and evaluated with 10433 retrospectively collected radiographs and labelled from echocardiography reports. The ensemble AI model could detect aortic stenosis in a test dataset with an AUC of 0.83 (95% CI 0.78–0.88). Since chest radiography is a cost effective and widely available imaging test, our model can provide an additive resource for the detection of aortic stenosis.

Sign in / Sign up

Export Citation Format

Share Document