Texture Analysis in the Evaluation of Covid-19 Pneumonia in Chest X-Ray Images: a Proof of Concept Study

2021 ◽  
Vol 17 ◽  
Author(s):  
Armando Ugo Cavallo ◽  
Jacopo Troisi ◽  
Marco Forcina ◽  
Pier-Valerio Mari ◽  
Valerio Forte ◽  
...  
Keyword(s):  
Machine Learning ◽  
Texture Analysis ◽  
Model Performance ◽  
Final Analysis ◽  
Machine Learning Algorithms ◽  
Screening Tools ◽  
X Ray ◽  
Additional Tool ◽  
Ensemble Machine Learning ◽  
Chest X Ray

Background: One of the most challenging aspects related to Covid-19 is to establish the presence of infection in early phase of the disease. Texture analysis might be an additional tool for the evaluation of Chest X-ray in patients with clinical suspicion of Covid-19 related pneumonia. Objective: To evaluate the diagnostic performance of texture analysis and machine learning models for the diagnosis of Covid-19 interstitial pneumonia in Chest X-ray images. Methods: Chest X-ray images were accessed from a publicly available repository (https://www.kaggle.com/tawsifurrahman/covid19-radiography-database). Lung areas were manually segmented using a polygonal regions of interest covering both lung areas, using MaZda, a freely available software for texture analysis. A total of 308 features per ROI was extracted. One hundred-ten Covid-19 Chest X-ray images were selected for the final analysis. Results: Six models, namely NB, GLM, DL, GBT, ANN and PLS-DA were selected and ensembled. According to Youden’s index, the Covid-19 Ensemble Machine Learning Score showing the highest Area Under the Curve (0.971±0.015) was 132.57. Assuming this cut-off the Ensemble model performance was estimated evaluating both true and false positive/negative, resulting in 91.8% accuracy with 93% sensitivity and 90% specificity. Moving the cut-off value to -100, although the accuracy resulted lower (90.6%), the Ensemble Machine Learning showed 100% sensitivity, with 80% specificity. Conclusion: Texture analysis of Chest X-ray images and machine learning algorithms may help in differentiating patients with Covid-19 pneumonia. Despite several limitations, this study can lay ground for future researches in this field and help developing more rapid and accurate screening tools for these patients.

scholarly journals Texture Analysis in the Evaluation of Covid-19 Pneumonia in Chest X-Ray Images: a Proof of Concept Study

2020 ◽  
Author(s):  
Armando Ugo Cavallo ◽  
Jacopo Troisi ◽  
Marco Forcina ◽  
Piervalerio Mari ◽  
Valerio Forte ◽  
...  
Keyword(s):  
Machine Learning ◽  
Texture Analysis ◽  
Model Performance ◽  
Final Analysis ◽  
Machine Learning Algorithms ◽  
Screening Tools ◽  
X Ray ◽  
Additional Tool ◽  
Ensemble Machine Learning ◽  
Chest X Ray

Abstract Objectives: One of the most challenging aspects related to Covid-19 is to establish the presence of infection in early phase of the disease. Recently Artificial Intelligence systems for the diagnosis of Covid-19 related pneumonia on Chest X ray (CXR) or chest CT have been tested with variable, but not negligible, accuracy. Texture analysis might be an additional tool for the evaluation of CXR in patients with clinical suspicion of Covid-19 related pneumonia.Methods: CXR images were accessed from a publicly available repository (https://www.kaggle.com/tawsifurrahman/covid19-radiography-database). Lung areas were manually segmented using a polygonal regions of interest (ROI) covering both lung areas, using MaZda, a freely available software for texture analysis. A total of 308 features per ROI was extracted. One hundred-ten Covid-19 CXR images were selected for the final analysisResults: Six models, namely NB, GLM, DL, GBT, ANN and PLS-DA were selected and ensembled. According to Youden’s index, the Covid-19 Ensemble Machine Learning (EML)-Score showing the highest AUCROC (0.971±0.015) was 132.57. Assuming this cut-off the EML model performance was estimated evaluating both true and false positive/negative, resulting in 91.8% accuracy with 93% sensitivity and 90% specificity. Moving the cut-off value to -100, although the accuracy resulted lower (90.6%), the EML showed 100% sensitivity, with 80% specificityConclusion: Texture analysis of CXR images and machine learning algorithms may help in differentiating patients with Covid-19 pneumonia. Despite several limitations, this study can lay ground for future researches in this field and help developing more rapid and accurate screening tools for these patients.

scholarly journals Intelligent Identification for Rock-Mineral Microscopic Images Using Ensemble Machine Learning Algorithms

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3914 ◽  
Author(s):  
Ye Zhang ◽  
Mingchao Li ◽  
Shuai Han ◽  
Qiubing Ren ◽  
Jonathan Shi
Keyword(s):  
Machine Learning ◽  
Model Performance ◽  
Image Features ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Microscopic Images ◽  
Rock Mineral ◽  
Ensemble Machine Learning ◽  
Significant Performance

It is significant to identify rock-mineral microscopic images in geological engineering. The task of microscopic mineral image identification, which is often conducted in the lab, is tedious and time-consuming. Deep learning and convolutional neural networks (CNNs) provide a method to analyze mineral microscopic images efficiently and smartly. In this research, the transfer learning model of mineral microscopic images is established based on Inception-v3 architecture. The four mineral image features, including K-feldspar (Kf), perthite (Pe), plagioclase (Pl), and quartz (Qz or Q), are extracted using Inception-v3. Based on the features, the machine learning methods, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN), multilayer perceptron (MLP), and gaussian naive Bayes (GNB), are adopted to establish the identification models. The results are evaluated using 10-fold cross-validation. LR, SVM, and MLP have a significant performance among all the models, with accuracy of about 90.0%. The evaluation result shows LR, SVM, and MLP are the outstanding single models in high-dimensional feature analysis. The three models are also selected as the base models in model stacking. The LR model is also set as the meta classifier in the final prediction. The stacking model can achieve 90.9% accuracy, which is higher than all the single models. The result also shows that model stacking effectively improves model performance.

scholarly journals Comparison of machine learning algorithms for chest X-ray image COVID-19 classification

2021 ◽  
Vol 1933 (1) ◽  
pp. 012040
Author(s):  
Samsir Samsir ◽  
Jimmi Hendrik P. Sitorus ◽  
Zulkifli ◽  
Zuriani Ritonga ◽  
Fitri Aini Nasution ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
X Ray ◽  
Chest X Ray

scholarly journals A Prediction of Corona Disease Transmission Using A Traditional Machine Learning Approach

2020 ◽  
pp. 447-452
Author(s):  
Chandran Venkatesan ◽  
Elakkiya Balan ◽  
Sumithra M G ◽  
Karthick A ◽  
Jayarajan V ◽  
...  
Keyword(s):  
Machine Learning ◽  
Disease Transmission ◽  
Transition Process ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
X Ray ◽  
Machine Learning Approach ◽  
Current Scenario ◽  
Chest X Ray ◽  
Bayes Algorithm

In this current scenario, covid pandemic breaks analysis is becoming popular among the researchers. The various data sources from the different countries analyzed to predict the possibility of coronavirus transition from one person to another person. The datasets are not providing more information about the causes of the corona. Many authors provided the solution by using chest X-ray and CT images to predict the corona. In this paper, the covid pandemic transition process from one person to another person was classified using traditional machine learning algorithms. The input labels are encoded and transformed, utilizing the label encoder technique. The XG boost algorithm was outperformed all the other algorithms with overall accuracy and F1-measure of 99%. The Naive Bayes algorithm provides 100% accuracy, precision, recall, and F1-Score due to its improved ability to handle lower datasets.

scholarly journals Explainable Machine Learning for COVID-19 Pneumonia Classification With Texture-Based Features Extraction in Chest Radiography

2022 ◽  
Vol 3 ◽  
Author(s):  
Luís Vinícius de Moura ◽  
Christian Mattjie ◽  
Caroline Machado Dartora ◽  
Rodrigo C. Barros ◽  
Ana Maria Marques da Silva
Keyword(s):  
Machine Learning ◽  
Random Search ◽  
Texture Features ◽  
Classification Model ◽  
Recursive Feature Elimination ◽  
X Rays ◽  
X Ray ◽  
Reverse Transcription Pcr ◽  
Ensemble Machine Learning ◽  
Chest X Ray

Both reverse transcription-PCR (RT-PCR) and chest X-rays are used for the diagnosis of the coronavirus disease-2019 (COVID-19). However, COVID-19 pneumonia does not have a defined set of radiological findings. Our work aims to investigate radiomic features and classification models to differentiate chest X-ray images of COVID-19-based pneumonia and other types of lung patterns. The goal is to provide grounds for understanding the distinctive COVID-19 radiographic texture features using supervised ensemble machine learning methods based on trees through the interpretable Shapley Additive Explanations (SHAP) approach. We use 2,611 COVID-19 chest X-ray images and 2,611 non-COVID-19 chest X-rays. After segmenting the lung in three zones and laterally, a histogram normalization is applied, and radiomic features are extracted. SHAP recursive feature elimination with cross-validation is used to select features. Hyperparameter optimization of XGBoost and Random Forest ensemble tree models is applied using random search. The best classification model was XGBoost, with an accuracy of 0.82 and a sensitivity of 0.82. The explainable model showed the importance of the middle left and superior right lung zones in classifying COVID-19 pneumonia from other lung patterns.

scholarly journals Predicting Future Occurrence of Acute Hypotensive Episodes Using Noninvasive and Invasive Features

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 445-451
Author(s):  
Yifei Sun ◽  
Navid Rashedi ◽  
Vikrant Vaze ◽  
Parikshit Shah ◽  
Ryan Halter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Real World ◽  
Short Term Memory ◽  
Model Performance ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Continuous Map

ABSTRACT Introduction Early prediction of the acute hypotensive episode (AHE) in critically ill patients has the potential to improve outcomes. In this study, we apply different machine learning algorithms to the MIMIC III Physionet dataset, containing more than 60,000 real-world intensive care unit records, to test commonly used machine learning technologies and compare their performances. Materials and Methods Five classification methods including K-nearest neighbor, logistic regression, support vector machine, random forest, and a deep learning method called long short-term memory are applied to predict an AHE 30 minutes in advance. An analysis comparing model performance when including versus excluding invasive features was conducted. To further study the pattern of the underlying mean arterial pressure (MAP), we apply a regression method to predict the continuous MAP values using linear regression over the next 60 minutes. Results Support vector machine yields the best performance in terms of recall (84%). Including the invasive features in the classification improves the performance significantly with both recall and precision increasing by more than 20 percentage points. We were able to predict the MAP with a root mean square error (a frequently used measure of the differences between the predicted values and the observed values) of 10 mmHg 60 minutes in the future. After converting continuous MAP predictions into AHE binary predictions, we achieve a 91% recall and 68% precision. In addition to predicting AHE, the MAP predictions provide clinically useful information regarding the timing and severity of the AHE occurrence. Conclusion We were able to predict AHE with precision and recall above 80% 30 minutes in advance with the large real-world dataset. The prediction of regression model can provide a more fine-grained, interpretable signal to practitioners. Model performance is improved by the inclusion of invasive features in predicting AHE, when compared to predicting the AHE based on only the available, restricted set of noninvasive technologies. This demonstrates the importance of exploring more noninvasive technologies for AHE prediction.

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