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Singh S, Kumar M, Kumar A, Verma BK, Abhishek K, Selvarajan S. Efficient pneumonia detection using Vision Transformers on chest X-rays. Sci Rep 2024; 14:2487. [PMID: 38291130 PMCID: PMC10827725 DOI: 10.1038/s41598-024-52703-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 01/22/2024] [Indexed: 02/01/2024] Open
Abstract
Pneumonia is a widespread and acute respiratory infection that impacts people of all ages. Early detection and treatment of pneumonia are essential for avoiding complications and enhancing clinical results. We can reduce mortality, improve healthcare efficiency, and contribute to the global battle against a disease that has plagued humanity for centuries by devising and deploying effective detection methods. Detecting pneumonia is not only a medical necessity but also a humanitarian imperative and a technological frontier. Chest X-rays are a frequently used imaging modality for diagnosing pneumonia. This paper examines in detail a cutting-edge method for detecting pneumonia implemented on the Vision Transformer (ViT) architecture on a public dataset of chest X-rays available on Kaggle. To acquire global context and spatial relationships from chest X-ray images, the proposed framework deploys the ViT model, which integrates self-attention mechanisms and transformer architecture. According to our experimentation with the proposed Vision Transformer-based framework, it achieves a higher accuracy of 97.61%, sensitivity of 95%, and specificity of 98% in detecting pneumonia from chest X-rays. The ViT model is preferable for capturing global context, comprehending spatial relationships, and processing images that have different resolutions. The framework establishes its efficacy as a robust pneumonia detection solution by surpassing convolutional neural network (CNN) based architectures.
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Affiliation(s)
| | - Manoj Kumar
- JSS Academy of Technical Education, Noida, India
| | - Abhay Kumar
- National Institute of Technology Patna, Patna, India
| | | | | | - Shitharth Selvarajan
- School of Built Environment, Engineering and Computing, Leeds Beckett University, LS1 3HE, Leeds, UK.
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Hu X, Cao Y, Hu W, Zhang W, Li J, Wang C, Mukhopadhyay SC, Li Y, Liu Z, Li S. Refined Feature-based Multi-frame and Multi-scale Fusing Gate network for accurate segmentation of plaques in ultrasound videos. Comput Biol Med 2023; 163:107091. [PMID: 37331099 DOI: 10.1016/j.compbiomed.2023.107091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 04/29/2023] [Accepted: 05/27/2023] [Indexed: 06/20/2023]
Abstract
The accurate segmentation of carotid plaques in ultrasound videos will provide evidence for clinicians to evaluate the properties of plaques and treat patients effectively. However, the confusing background, blurry boundaries and plaque movement in ultrasound videos make accurate plaque segmentation challenging. To address the above challenges, we propose the Refined Feature-based Multi-frame and Multi-scale Fusing Gate Network (RMFG_Net), which captures spatial and temporal features in consecutive video frames for high-quality segmentation results and no manual annotation of the first frame. A spatial-temporal feature filter is proposed to suppress the noise of low-level CNN features and promote the detailed target area. To obtain a more accurate plaque position, we propose a transformer-based cross-scale spatial location algorithm, which models the relationship between adjacent layers of consecutive video frames to achieve stable positioning. To make full use of more detailed and semantic information, multi-layer gated computing is applied to fuse features of different layers, ensuring sufficient useful feature map aggregation for segmentation. Experiments on two clinical datasets demonstrate that the proposed method outperforms other state-of-the-art methods under different evaluation metrics, and it processes images with a speed of 68 frames per second which is suitable for real-time segmentation. A large number of ablation experiments were conducted to demonstrate the effectiveness of each component and experimental setting, as well as the potential of the proposed method in ultrasound video plaque segmentation tasks. The codes can be publicly available from https://github.com/xifengHuu/RMFG_Net.git.
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Affiliation(s)
- Xifeng Hu
- School of Information Science and Engineering, Shandong University, Qingdao 266237, China
| | - Yankun Cao
- School of Software, Shandong University, Jinan 250101, China
| | - Weifeng Hu
- School of Information Science and Engineering, Shandong University, Qingdao 266237, China
| | - Wenzhen Zhang
- School of Information Science and Engineering, Shandong University, Qingdao 266237, China
| | - Jing Li
- Beijing Hospital National Geriatrics Center, No. 1 Dahua Road, Dongcheng District, Beijing 100730, China
| | - Chuanyu Wang
- Beijing Hospital National Geriatrics Center, No. 1 Dahua Road, Dongcheng District, Beijing 100730, China
| | | | - Yujun Li
- School of Information Science and Engineering, Shandong University, Qingdao 266237, China.
| | - Zhi Liu
- School of Information Science and Engineering, Shandong University, Qingdao 266237, China.
| | - Shuo Li
- School of Case Western Reserve University, Cleveland, OH, USA
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Singh S, Kumar M, Kumar A, Verma BK, Shitharth S. Pneumonia detection with QCSA network on chest X-ray. Sci Rep 2023; 13:9025. [PMID: 37270553 DOI: 10.1038/s41598-023-35922-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 05/25/2023] [Indexed: 06/05/2023] Open
Abstract
Worldwide, pneumonia is the leading cause of infant mortality. Experienced radiologists use chest X-rays to diagnose pneumonia and other respiratory diseases. The diagnostic procedure's complexity causes radiologists to disagree with the decision. Early diagnosis is the only feasible strategy for mitigating the disease's impact on the patent. Computer-aided diagnostics improve the accuracy of diagnosis. Recent studies established that Quaternion neural networks classify and predict better than real-valued neural networks, especially when dealing with multi-dimensional or multi-channel input. The attention mechanism has been derived from the human brain's visual and cognitive ability in which it focuses on some portion of the image and ignores the rest portion of the image. The attention mechanism maximizes the usage of the image's relevant aspects, hence boosting classification accuracy. In the current work, we propose a QCSA network (Quaternion Channel-Spatial Attention Network) by combining the spatial and channel attention mechanism with Quaternion residual network to classify chest X-Ray images for Pneumonia detection. We used a Kaggle X-ray dataset. The suggested architecture achieved 94.53% accuracy and 0.89 AUC. We have also shown that performance improves by integrating the attention mechanism in QCNN. Our results indicate that our approach to detecting pneumonia is promising.
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Affiliation(s)
| | - Manoj Kumar
- JSS Academy of Technical Education, Noida, India
| | - Abhay Kumar
- National Institute of Technology Patna, Patna, India
| | | | - S Shitharth
- Kebri Dehar University, Kebri Dehar, Ethiopia.
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Image-Based Pain Intensity Estimation Using Parallel CNNs with Regional Attention. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9120804. [PMID: 36551010 PMCID: PMC9774603 DOI: 10.3390/bioengineering9120804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 11/28/2022] [Accepted: 12/09/2022] [Indexed: 12/23/2022]
Abstract
Automatic pain estimation plays an important role in the field of medicine and health. In the previous studies, most of the entire image frame was directly imported into the model. This operation can allow background differences to negatively affect the experimental results. To tackle this issue, we propose the parallel CNNs framework with regional attention for automatic pain intensity estimation at the frame level. This modified convolution neural network structure combines BlurPool methods to enhance translation invariance in network learning. The improved networks can focus on learning core regions while supplementing global information, thereby obtaining parallel feature information. The core regions are mainly based on the tradeoff between the weights of the channel attention modules and the spatial attention modules. Meanwhile, the background information of the non-core regions is shielded by the DropBlock algorithm. These steps enable the model to learn facial pain features adaptively, not limited to a single image pattern. The experimental result of our proposed model outperforms many state-of-the-art methods on the RMSE and PCC metrics when evaluated on the diverse pain levels of over 12,000 images provided by the publicly available UNBC dataset. The model accuracy rate has reached 95.11%. The experimental results show that the proposed method is highly efficient at extracting the facial features of pain and predicts pain levels with high accuracy.
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Chen Y, Wang J, Wu Z, Lin Y. Integrating User-Group relationships under interest similarity constraints for social recommendation. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Feng Y, Zheng R, Cui X, Wang T, Jiang T, Gao F, Cao J. 3D residual-attention-deep-network-based childhood epilepsy syndrome classification. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108856] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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Wu L, Hu S, Liu C. MR brain segmentation based on DE-ResUnet combining texture features and background knowledge. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103541] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Yang Y, Qi Y. Hierarchical accumulation network with grid attention for image super-resolution. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107520] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Wang C, Shen HZ, Fan F, Shao MW, Yang CS, Luo JC, Deng LJ. EAA-Net: A novel edge assisted attention network for single image dehazing. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107279] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Liu X, Jiao L, Li L, Tang X, Guo Y. Deep multi-level fusion network for multi-source image pixel-wise classification. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.106921] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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Shu Y, Zhang J, Xiao B, Li W. Medical image segmentation based on active fusion-transduction of multi-stream features. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.106950] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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