A deep learning ensemble framework for robust classification of lung ultrasound patterns: covid-19, pneumonia, and normal
To advance the automated interpretation of lung ultrasound (LUS) data, multiple deep learning (DL) models have been introduced to identify LUS patterns for differentiating COVID-19, Pneumonia, and Normal cases. While these models have generally yielded promising outcomes, they have encountered chall...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universitas Ahmad Dahlan
2025-02-01
|
| Series: | IJAIN (International Journal of Advances in Intelligent Informatics) |
| Subjects: | |
| Online Access: | https://ijain.org/index.php/IJAIN/article/view/1966 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850240702304223232 |
|---|---|
| author | Shereen Morsy Neveen Abd-Elsalam Ahmed Khandil Ahmed Elbialy Abou-Bakr Youssef |
| author_facet | Shereen Morsy Neveen Abd-Elsalam Ahmed Khandil Ahmed Elbialy Abou-Bakr Youssef |
| author_sort | Shereen Morsy |
| collection | DOAJ |
| description | To advance the automated interpretation of lung ultrasound (LUS) data, multiple deep learning (DL) models have been introduced to identify LUS patterns for differentiating COVID-19, Pneumonia, and Normal cases. While these models have generally yielded promising outcomes, they have encountered challenges in accurately classifying each pattern across diverse cases. Therefore, this study introduces an ensemble framework that leverages multiple classification models, optimizing their contributions to the final prediction through a majority voting mechanism. After training seven different classification models, the three models with the highest accuracies were selected. The ensemble incorporates these top-performing models: EfficientNetV2-B0, EfficientNetV2-B2, and EfficientNetV2-B3, and utilizes this framework to classify patterns in LUS images. Compared to individual model performance, the ensemble approach significantly enhances classification accuracy, achieving an accuracy of 99.25% and an F1-score of 99%. In contrast, the standalone models attained accuracies of 97.8%, 97.6%, and 98.1%, with F1-score of approximately 98%. This research highlights the potential of ensemble learning for improving the accuracy and robustness of automated LUS analysis, offering a practical and scalable solution for real-world medical diagnostics. By combining the strengths of multiple models, the proposed framework paves the way for more reliable and efficient tools to assist clinicians in diagnosing lung diseases. |
| format | Article |
| id | doaj-art-e2ea410263ba46b9bac6697dbdeb1cb8 |
| institution | OA Journals |
| issn | 2442-6571 2548-3161 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Universitas Ahmad Dahlan |
| record_format | Article |
| series | IJAIN (International Journal of Advances in Intelligent Informatics) |
| spelling | doaj-art-e2ea410263ba46b9bac6697dbdeb1cb82025-08-20T02:00:47ZengUniversitas Ahmad DahlanIJAIN (International Journal of Advances in Intelligent Informatics)2442-65712548-31612025-02-0111114315610.26555/ijain.v11i1.1966326A deep learning ensemble framework for robust classification of lung ultrasound patterns: covid-19, pneumonia, and normalShereen Morsy0Neveen Abd-Elsalam1Ahmed Khandil2Ahmed Elbialy3Abou-Bakr Youssef4Systems and Biomedical Engineering, Cairo UniversitySystems and Biomedical Engineering, Cairo UniversitySystems and Biomedical Engineering, Cairo UniversitySystems and Biomedical Engineering, Cairo University; and Shorouk AcademySystems and Biomedical Engineering, Cairo UniversityTo advance the automated interpretation of lung ultrasound (LUS) data, multiple deep learning (DL) models have been introduced to identify LUS patterns for differentiating COVID-19, Pneumonia, and Normal cases. While these models have generally yielded promising outcomes, they have encountered challenges in accurately classifying each pattern across diverse cases. Therefore, this study introduces an ensemble framework that leverages multiple classification models, optimizing their contributions to the final prediction through a majority voting mechanism. After training seven different classification models, the three models with the highest accuracies were selected. The ensemble incorporates these top-performing models: EfficientNetV2-B0, EfficientNetV2-B2, and EfficientNetV2-B3, and utilizes this framework to classify patterns in LUS images. Compared to individual model performance, the ensemble approach significantly enhances classification accuracy, achieving an accuracy of 99.25% and an F1-score of 99%. In contrast, the standalone models attained accuracies of 97.8%, 97.6%, and 98.1%, with F1-score of approximately 98%. This research highlights the potential of ensemble learning for improving the accuracy and robustness of automated LUS analysis, offering a practical and scalable solution for real-world medical diagnostics. By combining the strengths of multiple models, the proposed framework paves the way for more reliable and efficient tools to assist clinicians in diagnosing lung diseases.https://ijain.org/index.php/IJAIN/article/view/1966covid-19pneumoniadeep learningtransfer learningensemble method |
| spellingShingle | Shereen Morsy Neveen Abd-Elsalam Ahmed Khandil Ahmed Elbialy Abou-Bakr Youssef A deep learning ensemble framework for robust classification of lung ultrasound patterns: covid-19, pneumonia, and normal IJAIN (International Journal of Advances in Intelligent Informatics) covid-19 pneumonia deep learning transfer learning ensemble method |
| title | A deep learning ensemble framework for robust classification of lung ultrasound patterns: covid-19, pneumonia, and normal |
| title_full | A deep learning ensemble framework for robust classification of lung ultrasound patterns: covid-19, pneumonia, and normal |
| title_fullStr | A deep learning ensemble framework for robust classification of lung ultrasound patterns: covid-19, pneumonia, and normal |
| title_full_unstemmed | A deep learning ensemble framework for robust classification of lung ultrasound patterns: covid-19, pneumonia, and normal |
| title_short | A deep learning ensemble framework for robust classification of lung ultrasound patterns: covid-19, pneumonia, and normal |
| title_sort | deep learning ensemble framework for robust classification of lung ultrasound patterns covid 19 pneumonia and normal |
| topic | covid-19 pneumonia deep learning transfer learning ensemble method |
| url | https://ijain.org/index.php/IJAIN/article/view/1966 |
| work_keys_str_mv | AT shereenmorsy adeeplearningensembleframeworkforrobustclassificationoflungultrasoundpatternscovid19pneumoniaandnormal AT neveenabdelsalam adeeplearningensembleframeworkforrobustclassificationoflungultrasoundpatternscovid19pneumoniaandnormal AT ahmedkhandil adeeplearningensembleframeworkforrobustclassificationoflungultrasoundpatternscovid19pneumoniaandnormal AT ahmedelbialy adeeplearningensembleframeworkforrobustclassificationoflungultrasoundpatternscovid19pneumoniaandnormal AT aboubakryoussef adeeplearningensembleframeworkforrobustclassificationoflungultrasoundpatternscovid19pneumoniaandnormal AT shereenmorsy deeplearningensembleframeworkforrobustclassificationoflungultrasoundpatternscovid19pneumoniaandnormal AT neveenabdelsalam deeplearningensembleframeworkforrobustclassificationoflungultrasoundpatternscovid19pneumoniaandnormal AT ahmedkhandil deeplearningensembleframeworkforrobustclassificationoflungultrasoundpatternscovid19pneumoniaandnormal AT ahmedelbialy deeplearningensembleframeworkforrobustclassificationoflungultrasoundpatternscovid19pneumoniaandnormal AT aboubakryoussef deeplearningensembleframeworkforrobustclassificationoflungultrasoundpatternscovid19pneumoniaandnormal |