Automated diagnosis of respiratory diseases from lung ultrasound videos ensuring XAI: an innovative hybrid model approach
IntroductionAn automated computerized approach can aid radiologists in the early diagnosis of lung disease from video modalities. This study focuses on the difficulties associated with identifying and categorizing respiratory diseases, including COVID-19, influenza, and pneumonia.MethodsWe propose a...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Computer Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcomp.2024.1438126/full |
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| author | Arefin Ittesafun Abian Mohaimenul Azam Khan Raiaan Asif Karim Sami Azam Nur Mohammad Fahad Niusha Shafiabady Kheng Cher Yeo Friso De Boer |
| author_facet | Arefin Ittesafun Abian Mohaimenul Azam Khan Raiaan Asif Karim Sami Azam Nur Mohammad Fahad Niusha Shafiabady Kheng Cher Yeo Friso De Boer |
| author_sort | Arefin Ittesafun Abian |
| collection | DOAJ |
| description | IntroductionAn automated computerized approach can aid radiologists in the early diagnosis of lung disease from video modalities. This study focuses on the difficulties associated with identifying and categorizing respiratory diseases, including COVID-19, influenza, and pneumonia.MethodsWe propose a novel method that combines three dimensional (3D) models, model explainability (XAI), and a Decision Support System (DSS) that utilizes lung ultrasound (LUS) videos. The objective of the study is to improve the quality of video frames, boost the diversity of the dataset, maintain the sequence of frames, and create a hybrid 3D model [Three-Dimensional Time Distributed Convolutional Neural Network-Long short-term memory (TD-CNNLSTM-LungNet)] for precise classification. The proposed methodology involves applying morphological opening and contour detection to improve frame quality, utilizing geometrical augmentation for dataset balance, introducing a graph-based approach for frame sequencing, and implementing a hybrid 3D model combining time-distributed CNN and LSTM networks utilizing vast ablation study. Model explainability is ensured through heatmap generation, region of interest segmentation, and Probability Density Function (PDF) graphs illustrating feature distribution.ResultsOur model TD-CNN-LSTM-LungNet attained a remarkable accuracy of 96.57% in classifying LUS videos into pneumonia, COVID-19, normal, and other lung disease classes, which is above compared to ten traditional transfer learning models experimented with in this study. The eleven-ablation case study reduced training costs and redundancy. K-fold cross-validation and accuracy-loss curves demonstrated model generalization. The DSS, incorporating Layer Class Activation Mapping (LayerCAM) and heatmaps, improved interpretability and reliability, and PDF graphs facilitated precise decision-making by identifying feature boundaries. The DSS facilitates clinical marker analysis, and the validation by using the proposed algorithms highlights its impact on a reliable diagnosis outcome.DiscussionOur proposed methodology could assist radiologists in accurately detecting and comprehending the patterns of respiratory disorders. |
| format | Article |
| id | doaj-art-a97a1449750e446b862b0640c4e145d5 |
| institution | OA Journals |
| issn | 2624-9898 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Computer Science |
| spelling | doaj-art-a97a1449750e446b862b0640c4e145d52025-08-20T01:58:45ZengFrontiers Media S.A.Frontiers in Computer Science2624-98982024-12-01610.3389/fcomp.2024.14381261438126Automated diagnosis of respiratory diseases from lung ultrasound videos ensuring XAI: an innovative hybrid model approachArefin Ittesafun Abian0Mohaimenul Azam Khan Raiaan1Asif Karim2Sami Azam3Nur Mohammad Fahad4Niusha Shafiabady5Kheng Cher Yeo6Friso De Boer7Department of Computer Science and Engineering, United International University, Dhaka, BangladeshDepartment of Computer Science and Engineering, United International University, Dhaka, BangladeshFaculty of Science and Technology, Charles Darwin University, Darwin, NT, AustraliaFaculty of Science and Technology, Charles Darwin University, Darwin, NT, AustraliaDepartment of Computer Science and Engineering, United International University, Dhaka, BangladeshDepartment of Information Technology, Australian Catholic University, North Sydney, NSW, AustraliaFaculty of Science and Technology, Charles Darwin University, Darwin, NT, AustraliaFaculty of Science and Technology, Charles Darwin University, Darwin, NT, AustraliaIntroductionAn automated computerized approach can aid radiologists in the early diagnosis of lung disease from video modalities. This study focuses on the difficulties associated with identifying and categorizing respiratory diseases, including COVID-19, influenza, and pneumonia.MethodsWe propose a novel method that combines three dimensional (3D) models, model explainability (XAI), and a Decision Support System (DSS) that utilizes lung ultrasound (LUS) videos. The objective of the study is to improve the quality of video frames, boost the diversity of the dataset, maintain the sequence of frames, and create a hybrid 3D model [Three-Dimensional Time Distributed Convolutional Neural Network-Long short-term memory (TD-CNNLSTM-LungNet)] for precise classification. The proposed methodology involves applying morphological opening and contour detection to improve frame quality, utilizing geometrical augmentation for dataset balance, introducing a graph-based approach for frame sequencing, and implementing a hybrid 3D model combining time-distributed CNN and LSTM networks utilizing vast ablation study. Model explainability is ensured through heatmap generation, region of interest segmentation, and Probability Density Function (PDF) graphs illustrating feature distribution.ResultsOur model TD-CNN-LSTM-LungNet attained a remarkable accuracy of 96.57% in classifying LUS videos into pneumonia, COVID-19, normal, and other lung disease classes, which is above compared to ten traditional transfer learning models experimented with in this study. The eleven-ablation case study reduced training costs and redundancy. K-fold cross-validation and accuracy-loss curves demonstrated model generalization. The DSS, incorporating Layer Class Activation Mapping (LayerCAM) and heatmaps, improved interpretability and reliability, and PDF graphs facilitated precise decision-making by identifying feature boundaries. The DSS facilitates clinical marker analysis, and the validation by using the proposed algorithms highlights its impact on a reliable diagnosis outcome.DiscussionOur proposed methodology could assist radiologists in accurately detecting and comprehending the patterns of respiratory disorders.https://www.frontiersin.org/articles/10.3389/fcomp.2024.1438126/fulllung ultrasoundCOVID-19LayerCAMdecision support systemLSTMCNN |
| spellingShingle | Arefin Ittesafun Abian Mohaimenul Azam Khan Raiaan Asif Karim Sami Azam Nur Mohammad Fahad Niusha Shafiabady Kheng Cher Yeo Friso De Boer Automated diagnosis of respiratory diseases from lung ultrasound videos ensuring XAI: an innovative hybrid model approach Frontiers in Computer Science lung ultrasound COVID-19 LayerCAM decision support system LSTM CNN |
| title | Automated diagnosis of respiratory diseases from lung ultrasound videos ensuring XAI: an innovative hybrid model approach |
| title_full | Automated diagnosis of respiratory diseases from lung ultrasound videos ensuring XAI: an innovative hybrid model approach |
| title_fullStr | Automated diagnosis of respiratory diseases from lung ultrasound videos ensuring XAI: an innovative hybrid model approach |
| title_full_unstemmed | Automated diagnosis of respiratory diseases from lung ultrasound videos ensuring XAI: an innovative hybrid model approach |
| title_short | Automated diagnosis of respiratory diseases from lung ultrasound videos ensuring XAI: an innovative hybrid model approach |
| title_sort | automated diagnosis of respiratory diseases from lung ultrasound videos ensuring xai an innovative hybrid model approach |
| topic | lung ultrasound COVID-19 LayerCAM decision support system LSTM CNN |
| url | https://www.frontiersin.org/articles/10.3389/fcomp.2024.1438126/full |
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