Multi-branch fusion auxiliary learning for the detection of pneumonia from chest X-ray images

Liu, Jia; Qi, Jing; Chen, Wei; Nian, Yongjian

doi:10.1016/j.compbiomed.2022.105732

Cited by 13 publications

(4 citation statements)

References 41 publications

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“…[ 7 ] Therefore, timely and effective pathogen detection methods are critical for improving the diagnosis rate and the effectiveness of targeted anti-infective therapy. [ 8 ] The high-throughput sequencing technology metagenomics next-generation sequencing (NGS or mNGS) provides an efficient and unbiased way to identify pathogens in host-associated and environmental samples. [ 9 ] The findings of proof-of-concept studies on NGS techniques have recently led to their application as a routine tool in the laboratory.…”

Section: Introductionmentioning

confidence: 99%

Comparison of next-generation sequencing with traditional methods for pathogen detection in cases of lower respiratory tract infection at a community hospital in Eastern China

et al. 2022

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Lower respiratory tract infection (LRTI) is still a threat to human health. Metagenomics next-generation sequencing (NGS) provides an efficient and unbiased way to identify LRTI pathogens, and has been shown to have several advantages over traditional methods. However, its application is currently limited in low-resource settings. Our aim was to collect and analyze data on LRTI cases at a county-level community hospital in Eastern China over one year, in order to compare the efficiency of NGS and traditional methods including culture, nucleic acid amplification and antibody techniques. We performed NGS of bronchoalveolar lavage fluid (BALF) for pathogen identification in 71 patients with LRTI. We compared the detection rates, identified pathogens, and turnaround time of NGS with traditional methods. Pathogens were detected using traditional methods in 19 cases, and the results were compared with those obtained with the NGS technique in 60 cases. The pathogen detection rate of NGS (84.5%) was much higher than that of the traditional methods (26.8%). Moreover, with the traditional methods considered the gold standard, the consistency rate between NGS and traditional methods was 68.4%. For the 19 cases in which the traditional method was used, the main pathogens included invasive Aspergillus (5 cases), Pseudomonas aeruginosa (3 cases), Candida albicans (3 cases), and Staphylococcus aureus (2 cases). Among the 60 cases detected by NGS, the main pathogens included Mycobacterium (12 cases), Streptococcus pneumoniae (5 cases), Klebsiella pneumoniae (3 cases), P. aeruginosa (3 cases), Haemophilus influenzae (3 cases), and S. aureus (3 cases), Aspergillus (9 cases), Pneumocystis jiroveci (5 cases), C. albicans (3 cases), Human Papilloma Virus (9 cases), Epstein-Barr virus (8 cases), and parvovirus (6 cases). In addition, 2 cases of chlamydia and 1 case of mycoplasma infection were detected by NGS. The time taken to perform the NGS tests was significantly shorter than that taken with the traditional method. NGS analysis of bronchoalveolar lavage fluid, in combination with traditional pathogen detection methods, can improve the efficiency of pathogen detection. More attention should be paid to the regional epidemic characteristics of infectious pathogens in LRTI.

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Section: Introductionmentioning

confidence: 99%

Comparison of next-generation sequencing with traditional methods for pathogen detection in cases of lower respiratory tract infection at a community hospital in Eastern China

et al. 2022

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“…Notably, the literature suggests selecting a threshold value that maximizes the recall, prioritizing its significance in the context of pneumonia detection. Liu and colleagues [8] introduced an inventive method, referred to as Multi-Branch Fusion Auxiliary Learning (MBFAL) to categorize samples of normal, COVID-19, other viral pneumonia, and bacterial pneumonia using chest X-ray (CXR) images. This approach incorporates a dual-branch network structure, where the primary task branch is dedicated to identifying the four categories, and the auxiliary task branch focuses on distinguishing between various pneumonia types, including COVID-19, other viral pneumonia, and bacterial pneumonia.…”

Section: Literature Review Sharma and Guleriamentioning

confidence: 99%

A Review on Deep Learning Based Automated Pneumonia Detection using X-ray images

et al. 2024

IJFMR

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This paper provides a comprehensive literature survey on the application of various deep learning models for pneumonia detection using chest X-ray images. It reviews numerous studies that propose different architectures and techniques aimed at enhancing detection accuracy and efficiency. The survey covers the use of methods such as transfer learning and ensemble techniques to mitigate data scarcity and augment model performance. It also discusses the development of advanced algorithms for the precise identification and localization of pneumonia indications. Despite promising results, the survey acknowledges persisting challenges, including the need for high accuracy in medical testing and the limited availability of annotated medical images. This review underscores the transformative potential of deep learning in pneumonia detection and identifies areas for future research.

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“…Their results give an accuracy of 97.4%. The results of another multiclassification were published in [28] in which 21057 CXR images were used to classify COVID, Pneumonia viral and bacterial, and normal. They proposed a multi-branch fusion auxiliary learning method and achieved an overall accuracy of 95.61%.…”

Section: Literature Surveymentioning

confidence: 99%

A compact CNN model for automated detection of COVID-19 using thorax x-ray images

Awan

Khan

Mannan³

2023

IFS

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COVID-19 is an epidemic, causing an enormous death toll. The mutational changing of an RNA virus is causing diagnostic complexities. RT-PCR and Rapid Tests are used for the diagnosis, but unfortunately, these methods are ineffective in diagnosing all strains of COVID-19. There is an utmost need to develop a diagnostic procedure for timely identification. In the proposed work, we come up with a lightweight algorithm based on deep learning to develop a rapid detection system for COVID-19 with thorax chest x-ray (CXR) images. This research aims to develop a fine-tuned convolutional neural network (CNN) model using improved EfficientNetB5. Design is based on compound scaling and trained on the best possible feature extraction algorithm. The low convergence rate of the proposed work can be easily deployed into limited computational resources. It will be helpful for the rapid triaging of victims. 2-fold cross-validation further improves the performance. The algorithm proposed is trained, validated, and testing is performed in the form of internal and external validation on a self-collected and compiled a real-time dataset of CXR. The training dataset is relatively extensive compared to the existing ones. The performance of the proposed technique is measured, validated, and compared with other state-of-the-art pre-trained models. The proposed methodology gives remarkable accuracy (99.5%) and recall (99.5%) for biclassification. The external validation using two different test dataset also give exceptional predictions. The visual depiction of predictions is represented by Grad-CAM maps, presenting the extracted features of the predicted results.

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Multi-branch fusion auxiliary learning for the detection of pneumonia from chest X-ray images

Cited by 13 publications

References 41 publications

Comparison of next-generation sequencing with traditional methods for pathogen detection in cases of lower respiratory tract infection at a community hospital in Eastern China

Comparison of next-generation sequencing with traditional methods for pathogen detection in cases of lower respiratory tract infection at a community hospital in Eastern China

A Review on Deep Learning Based Automated Pneumonia Detection using X-ray images

A compact CNN model for automated detection of COVID-19 using thorax x-ray images

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