A Systematic Review of Deep Learning Techniques for Tuberculosis Detection From Chest Radiograph

Oloko-Oba, Mustapha; Viriri, Serestina

doi:10.3389/fmed.2022.830515

Cited by 23 publications

(5 citation statements)

References 75 publications

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“…EfficientNetV2 [ 30 ] was adopted in our study, which had shown superior efficiency in previous studies [ 39 , 40 ]. Our CAD algorithm’s performance was further augmented through an ensemble [ 41 ], which was expected to prevent the algorithm from overfitting on a small dataset, thus improving its potential for external generalizability.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning–based Diagnosis of Pulmonary Tuberculosis on Chest X-ray in the Emergency Department: A Retrospective Study

Wang,

Chang,

Lee

et al. 2024

J Digit Imaging. Inform. med.

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Prompt and correct detection of pulmonary tuberculosis (PTB) is critical in preventing its spread. We aimed to develop a deep learning–based algorithm for detecting PTB on chest X-ray (CXRs) in the emergency department. This retrospective study included 3498 CXRs acquired from the National Taiwan University Hospital (NTUH). The images were chronologically split into a training dataset, NTUH-1519 (images acquired during the years 2015 to 2019; n = 2144), and a testing dataset, NTUH-20 (images acquired during the year 2020; n = 1354). Public databases, including the NIH ChestX-ray14 dataset (model training; 112,120 images), Montgomery County (model testing; 138 images), and Shenzhen (model testing; 662 images), were also used in model development. EfficientNetV2 was the basic architecture of the algorithm. Images from ChestX-ray14 were employed for pseudo-labelling to perform semi-supervised learning. The algorithm demonstrated excellent performance in detecting PTB (area under the receiver operating characteristic curve [AUC] 0.878, 95% confidence interval [CI] 0.854–0.900) in NTUH-20. The algorithm showed significantly better performance in posterior-anterior (PA) CXR (AUC 0.940, 95% CI 0.912–0.965, p-value < 0.001) compared with anterior–posterior (AUC 0.782, 95% CI 0.644–0.897) or portable anterior–posterior (AUC 0.869, 95% CI 0.814–0.918) CXR. The algorithm accurately detected cases of bacteriologically confirmed PTB (AUC 0.854, 95% CI 0.823–0.883). Finally, the algorithm tested favourably in Montgomery County (AUC 0.838, 95% CI 0.765–0.904) and Shenzhen (AUC 0.806, 95% CI 0.771–0.839). A deep learning–based algorithm could detect PTB on CXR with excellent performance, which may help shorten the interval between detection and airborne isolation for patients with PTB.

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Section: Discussionmentioning

confidence: 99%

Deep Learning–based Diagnosis of Pulmonary Tuberculosis on Chest X-ray in the Emergency Department: A Retrospective Study

Wang,

Chang,

Lee

et al. 2024

J Digit Imaging. Inform. med.

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“…The TBNet framework supports a contextaware graph network to diagnose TB. 8,9,[25][26][27] Thus, these research works demonstrated the potential of various image processing techniques and machine learning algorithms used in pulmonary image analysis, providing insights for the development of more accurate and efficient diagnostic tools for lung diseases.…”

Section: Review On Fuzzy Based Machine Learning Techniquesmentioning

confidence: 94%

“…It employed deep learning techniques to detect TB from chest radiograph. The TBNet framework supports a context‐aware graph network to diagnose TB 8,9,25–27 …”

Section: Related Workmentioning

confidence: 99%

Gray level fuzzy deep neural networks for enhancing performance in lung disease detection: A comparative study with fuzzy logic methods

Muthukumar,

Prasad,

Raju

et al. 2024

Int J Imaging Syst Tech

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Lung cancer is a deadly disease, and its early detection is crucial for effective treatment. In this context, accurate classification of lung cancers from computed tomography imaging is a vital research area. Irregularly detected gray matter in these images can affect classification outcomes, making accurate lung cancer detection difficult. To address this issue, researchers have developed a new approach that combines fuzzy logic and deep neural networks (DNNs) for extracting the hidden characteristic features of lung diseases from the images, and thereby, ensuring that only relevant features are used for classification. The proposed gray‐level fuzzy approach on DNNs (GL‐FDNN) is designed to accurately classify four distinguished lung cancer classes namely, Large cell carcinoma, Adenocarcinoma, Normal lung computed tomography, and Squamous cell carcinoma. After identifying the area of interest, its pixel intensity ratio is used to derive the fuzzy logic, which is then used to extract the most obscure gray matter features. Then, ResNet‐18 sorts the obscure features into categories and picks only relevant features to improve classification accuracy. The gray‐level fuzzy approach on DNNs technique was tested alongside some of the renowned, existing techniques for their relative performances. Experimental analysis was carried out on standard Kaggle datasets and the outcomes reveal that the proposed technique offers the highest level of lung disease classification accuracy (99.2%). It also improves the recall and precision factors. Thus, it can serve as a valuable diagnosis tool that can enhance the detection of lung cancer and the effectiveness of its treatment to save lives.

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“…However, these reviews are very specific and focus on single diseases like pneumonia (Khan et al. 2021 ), lung cancer (Pratim and Nachamai 2022 ), tuberculosis (Oloko-Oba and Viriri 2022 ), pulmonary nodule detection (Li et al. 2022 ), and COVID19 detection (Chen et al.…”

Section: Introductionmentioning

confidence: 99%

Review on chest pathogies detection systems using deep learning techniques

et al. 2023

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Chest radiography is the standard and most affordable way to diagnose, analyze, and examine different thoracic and chest diseases. Typically, the radiograph is examined by an expert radiologist or physician to decide about a particular anomaly, if exists. Moreover, computer-aided methods are used to assist radiologists and make the analysis process accurate, fast, and more automated. A tremendous improvement in automatic chest pathologies detection and analysis can be observed with the emergence of deep learning. The survey aims to review, technically evaluate, and synthesize the different computer-aided chest pathologies detection systems. The state-of-the-art of single and multi-pathologies detection systems, which are published in the last five years, are thoroughly discussed. The taxonomy of image acquisition, dataset preprocessing, feature extraction, and deep learning models are presented. The mathematical concepts related to feature extraction model architectures are discussed. Moreover, the different articles are compared based on their contributions, datasets, methods used, and the results achieved. The article ends with the main findings, current trends, challenges, and future recommendations.

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A Systematic Review of Deep Learning Techniques for Tuberculosis Detection From Chest Radiograph

Cited by 23 publications

References 75 publications

Deep Learning–based Diagnosis of Pulmonary Tuberculosis on Chest X-ray in the Emergency Department: A Retrospective Study

Deep Learning–based Diagnosis of Pulmonary Tuberculosis on Chest X-ray in the Emergency Department: A Retrospective Study

Gray level fuzzy deep neural networks for enhancing performance in lung disease detection: A comparative study with fuzzy logic methods

Review on chest pathogies detection systems using deep learning techniques

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