Utilizing Pretrained Deep Learning Models for Automated Pulmonary Tuberculosis Detection Using Chest Radiography

Ho, Thi Kieu Khanh; Gwak, Jeonghwan; Prakash, Om; Song, Jung‐Han; Park, Chang Min

doi:10.1007/978-3-030-14802-7_34

Cited by 34 publications

(19 citation statements)

References 19 publications

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“…For stability, many approaches compared the performance of their models using different datasets whether for training, feature learning, validation, or all together. Examples of approaches in training, validation and all together are, respectively: Pan et al [62], Ho et al [133] and Gozes and Greenspan [166]. Some also experimented on different dataset sizes such as Shen et al [192].…”

Section: Results Interpretability and Training Datamentioning

confidence: 99%

“…Some also experimented on different dataset sizes such as Shen et al [192]. Performance was better when validated on internal datasets rather than external ones (using subset of dataset used for training) by Ho et al [133]; however, there is an optimal training dataset size for each situation. Pediatrics were introduced to chest diseases diagnosis as suggested by Candemir and Antani [7]; then, the model was trained with pediatrics and adults images resulting with good performance of the deep learning classifier by Kim et al [171].…”

Section: Results Interpretability and Training Datamentioning

confidence: 99%

“…However, the addition of adults images to training data did not increase the accuracy a lot (only increased 1.6%). Data augmentation was also used as one of the efficient pre-processing technique in many approaches to avoid over-fitting such as that of Zhao et al [138], Setio et al [148], Ho et al [133]. Bone shadow exclusion such as that of Gordienko et al [99] and normalization of pixel values like in that of Raju et al [182] also improved classification when applied to input images as pre-processing procedures.…”

Section: Results Interpretability and Training Datamentioning

confidence: 99%

“…Besides, Ho et al [133] compared the performance of three deep learning models (ResNet-152, Inception-ResNet and DenseNet-121) for automated detection of pulmonary TB and evaluated their efficiency on chest radiography diagnosis as early detection can reduce high mortality rates due to this disease. One training dataset (ChestXray14) and two external datasets (Montgomery and Shenzhen) were used for experimenting detection of diseased cases.…”

Section: General Thoracic Diseasesmentioning

confidence: 99%

See 3 more Smart Citations

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Farhat

Sakr

Kilany

2020

Machine Vision and Applications

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Shortly after deep learning algorithms were applied to Image Analysis, and more importantly to medical imaging, their applications increased significantly to become a trend. Likewise, deep learning applications (DL) on pulmonary medical images emerged to achieve remarkable advances leading to promising clinical trials. Yet, coronavirus can be the real trigger to open the route for fast integration of DL in hospitals and medical centers. This paper reviews the development of deep learning applications in medical image analysis targeting pulmonary imaging and giving insights of contributions to COVID-19. It covers more than 160 contributions and surveys in this field, all issued between February 2017 and May 2020 inclusively, highlighting various deep learning tasks such as classification, segmentation, and detection, as well as different pulmonary pathologies like airway diseases, lung cancer, COVID-19 and other infections. It summarizes and discusses the current state-of-the-art approaches in this research domain, highlighting the challenges, especially with COVID-19 pandemic current situation.

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Section: Results Interpretability and Training Datamentioning

confidence: 99%

Section: Results Interpretability and Training Datamentioning

confidence: 99%

Section: Results Interpretability and Training Datamentioning

confidence: 99%

Section: General Thoracic Diseasesmentioning

confidence: 99%

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Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Farhat

Sakr

Kilany

2020

Machine Vision and Applications

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“…Lopes et al [17] first proposed a method that combines pretrained CNNs and a Multiple Instance Learning algorithm to maximize the ability of CNNs to identify different types of pathologies in CXRs. Sivaramakrishnan et al [18] evaluated the performance of a CNN-based deep learning model for tuberculosis screening using CXRs, which is similar to [19]. Hwang et al [20] used transfer learning to improve the TB screening performance using convolutional neural networks.…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided System for the Detection of Multicategory Pulmonary Tuberculosis in Radiographs

Xie

Han

et al. 2020

Journal of Healthcare Engineering

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The early screening and diagnosis of tuberculosis plays an important role in the control and treatment of tuberculosis infections. In this paper, an integrated computer-aided system based on deep learning is proposed for the detection of multiple categories of tuberculosis lesions in chest radiographs. In this system, the fully convolutional neural network method is used to segment the lung area from the entire chest radiograph for pulmonary tuberculosis detection. Different from the previous analysis of the whole chest radiograph, we focus on the specific tuberculosis lesion areas for the analysis and propose the first multicategory tuberculosis lesion detection method. In it, a learning scalable pyramid structure is introduced into the Faster Region-based Convolutional Network (Faster RCNN), which effectively improves the detection of small-area lesions, mines indistinguishable samples during the training process, and uses reinforcement learning to reduce the detection of false-positive lesions. To compare our method with the current tuberculosis detection system, we propose a classification rule for whole chest X-rays using a multicategory tuberculosis lesion detection model and achieve good performance on two public datasets (Montgomery: AUC = 0.977 and accuracy = 0.926; Shenzhen: AUC = 0.941 and accuracy = 0.902). Our proposed computer-aided system is superior to current systems that can be used to assist radiologists in diagnoses and public health providers in screening for tuberculosis in areas where tuberculosis is endemic.

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Self-Rating Curriculum Learning for Localization and Segmentation of Tuberculosis on Chest Radiograph

Hong¹,

Guo²,

Lure

2022

Lecture Notes in Computer Science

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Utilizing Pretrained Deep Learning Models for Automated Pulmonary Tuberculosis Detection Using Chest Radiography

Cited by 34 publications

References 19 publications

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Computer-Aided System for the Detection of Multicategory Pulmonary Tuberculosis in Radiographs

Self-Rating Curriculum Learning for Localization and Segmentation of Tuberculosis on Chest Radiograph

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