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Rashid PQ, Türker İ. Lung Disease Detection Using U-Net Feature Extractor Cascaded by Graph Convolutional Network. Diagnostics (Basel) 2024; 14:1313. [PMID: 38928728 PMCID: PMC11202625 DOI: 10.3390/diagnostics14121313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 06/17/2024] [Accepted: 06/18/2024] [Indexed: 06/28/2024] Open
Abstract
Computed tomography (CT) scans have recently emerged as a major technique for the fast diagnosis of lung diseases via image classification techniques. In this study, we propose a method for the diagnosis of COVID-19 disease with improved accuracy by utilizing graph convolutional networks (GCN) at various layer formations and kernel sizes to extract features from CT scan images. We apply a U-Net model to aid in segmentation and feature extraction. In contrast with previous research retrieving deep features from convolutional filters and pooling layers, which fail to fully consider the spatial connectivity of the nodes, we employ GCNs for classification and prediction to capture spatial connectivity patterns, which provides a significant association benefit. We handle the extracted deep features to form an adjacency matrix that contains a graph structure and pass it to a GCN along with the original image graph and the largest kernel graph. We combine these graphs to form one block of the graph input and then pass it through a GCN with an additional dropout layer to avoid overfitting. Our findings show that the suggested framework, called the feature-extracted graph convolutional network (FGCN), performs better in identifying lung diseases compared to recently proposed deep learning architectures that are not based on graph representations. The proposed model also outperforms a variety of transfer learning models commonly used for medical diagnosis tasks, highlighting the abstraction potential of the graph representation over traditional methods.
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Affiliation(s)
| | - İlker Türker
- Department of Computer Engineering, Karabuk University, 78050 Karabuk, Turkey;
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Garg A, Salehi S, Rocca ML, Garner R, Duncan D. Efficient and visualizable convolutional neural networks for COVID-19 classification using Chest CT. EXPERT SYSTEMS WITH APPLICATIONS 2022; 195:116540. [PMID: 35075334 PMCID: PMC8769906 DOI: 10.1016/j.eswa.2022.116540] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 08/17/2021] [Accepted: 01/10/2022] [Indexed: 05/02/2023]
Abstract
With coronavirus disease 2019 (COVID-19) cases rising rapidly, deep learning has emerged as a promising diagnosis technique. However, identifying the most accurate models to characterize COVID-19 patients is challenging because comparing results obtained with different types of data and acquisition processes is non-trivial. In this paper we designed, evaluated, and compared the performance of 20 convolutional neutral networks in classifying patients as COVID-19 positive, healthy, or suffering from other pulmonary lung infections based on chest computed tomography (CT) scans, serving as the first to consider the EfficientNet family for COVID-19 diagnosis and employ intermediate activation maps for visualizing model performance. All models are trained and evaluated in Python using 4173 chest CT images from the dataset entitled "A COVID multiclass dataset of CT scans," with 2168, 758, and 1247 images of patients that are COVID-19 positive, healthy, or suffering from other pulmonary infections, respectively. EfficientNet-B5 was identified as the best model with an F1 score of 0.9769 ± 0.0046, accuracy of 0.9759 ± 0.0048, sensitivity of 0.9788 ± 0.0055, specificity of 0.9730 ± 0.0057, and precision of 0.9751 ± 0.0051. On an alternate 2-class dataset, EfficientNetB5 obtained an accuracy of 0.9845 ± 0.0109, F1 score of 0.9599 ± 0.0251, sensitivity of 0.9682 ± 0.0099, specificity of 0.9883 ± 0.0150, and precision of 0.9526 ± 0.0523. Intermediate activation maps and Gradient-weighted Class Activation Mappings offered human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools. With a prediction speed of under 0.1 s on GPUs and 0.5 s on CPUs, our proposed model offers a rapid, scalable, and accurate diagnostic for COVID-19.
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Affiliation(s)
- Aksh Garg
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, USA
- Stanford University, 450 Serra Mall, Stanford, California, USA
| | - Sana Salehi
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, USA
| | - Marianna La Rocca
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, USA
- Dipartimento Interateneo di Fisica, Università di Bari, Bari, Italy
| | - Rachael Garner
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, USA
| | - Dominique Duncan
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, USA
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