Deep-learning: A potential method for tuberculosis detection using chest radiography

Hooda, Rahul; Sofat, Sanjeev; Kaur, Simranpreet; Mittal, Ajay; Mériaudeau, Fabrice

doi:10.1109/icsipa.2017.8120663

Cited by 95 publications

(51 citation statements)

References 14 publications

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“…Unlike many other areas, the literature of TB detection through DL remains limited. The recent works are proposed by Lakhani and Sundaram [13], Hooda et al [28], Liu et al [14], Lopes and Valiati [12], and Rajaraman and Antani [42] to name a few. Lakhani and Sundaram [13] evaluated the efficacy of CNN for detecting TB.…”

Section: B Tb Detection Using Pre-trained Cnn (Transfer Learning)mentioning

confidence: 99%

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Image Enhancement for Tuberculosis Detection Using Deep Learning

et al. 2020

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The latest World Health Organization's (WHO) study on 2018 is showing that about 1.5 million people died and around 10 million people are infected with tuberculosis (TB) each year. Moreover, more than 4,000 people die every day from TB. A number of those deaths could have been stopped if the disease was identified sooner. In the recent literature, important work can be found on automating the diagnosis by applying techniques of deep learning (DL) to the medical images. While DL has yielded promising results in many areas, comprehensive TB diagnostic studies remain limited. DL requires a large number of high-quality training samples to yield better performance. Due to the low contrast of TB chest x-ray (CXR) images, they are often is in poor quality. This work assesses the effect of image enhancement on performance of DL technique to address this problem. The employed image enhancement algorithm was able to highlight the overall or local characteristics of the images, including some interesting features. Specifically, three image enhancement algorithms called Unsharp Masking (UM), High-Frequency Emphasis Filtering (HEF) and Contrast Limited Adaptive Histogram Equalization (CLAHE), were evaluated. The enhanced image samples were then fed to the pre-trained ResNet and EfficientNet models for transfer learning. In a TB image dataset, we achieved 89.92% and 94.8% of classification accuracy and AUC (Area Under Curve) scores, respectively. All the results are obtained using Shenzhen dataset, which are available in the public domain.

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Section: B Tb Detection Using Pre-trained Cnn (Transfer Learning)mentioning

confidence: 99%

“…Both untrained and pretrained on ImageNet were used and augmented with numerous pre-processing techniques. Hooda et al [28] employed 7 convolution layers and 3 FC layers of CNN architecture. They compared three different optimizers, and Adam optimizer achieved the best results.…”

Section: B Tb Detection Using Pre-trained Cnn (Transfer Learning)mentioning

confidence: 99%

Image Enhancement for Tuberculosis Detection Using Deep Learning

et al. 2020

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“…Its ability to learn features at the higher level has been shown to produce better results. Examples of work that employs deep learning, in particular CNN, to detect tuberculosis can be found in [16] and [17]. Using a customized CNN produced the best accuracy of 82.09% [16].…”

Section: Introductionmentioning

confidence: 99%

“…Examples of work that employs deep learning, in particular CNN, to detect tuberculosis can be found in [16] and [17]. Using a customized CNN produced the best accuracy of 82.09% [16]. In [17], an optimized CNN architecture was proposed where they manage to reduce the amount of computation without sacrificing the performance.…”

Section: Introductionmentioning

confidence: 99%

Ensemble deep learning for tuberculosis detection

Hijazi

Yang

Alfred

et al. 2020

IJEECS

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Tuberculosis (TB) is one of the deadliest infectious disease in the world. TB is caused by a type of tubercle bacillus called Mycobacterium Tuberculosis. Early detection of TB is pivotal to decrease the morbidity and mortality. TB is diagnosed by using the chest x-ray and a sputum test. Challenges for radiologists are to avoid confused and misdiagnose TB and lung cancer because they mimic each other. Semi-automated TB detection using machine learning found in the literature requires identification of objects of interest. The similarity of tissues, veins and small nodules presenting the image at the initial stage may hamper the detection. In this paper, an approach to detect TB, that does not require segmentation of objects of interest, based on ensemble deep learning, is presented. Evaluation on publicly available datasets show that the proposed approach produced a model that recorded the best accuracy, sensitivity and specificity of 91.0%, 89.6% and 90.7% respectively.

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“…There are various significant techniques for assisting in detection operation for chest radiographs [10]- [15]. The detection process is mainly dependent on segmentation techniques [16] and feature-based aspects [17] while the classification techniques are more into involving machine learning approaches [18]- [20]. However, there are various associated problems of existing classification techniques that are found unspoken in any research papers.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Classification Model for Diagnosing Multiple Disease Condition from Chest X-Ray

Savitha¹

2018

ijacsa

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Classification plays a significant role in the diagnosis of any form of radiological images in the healthcare sector. After reviewing existing classification approaches carried out over chest radiographs, it was explored that existing techniques are highly restricted to perform binary classification that is not comprehensive for assisting in an effective diagnosis process of chest disease condition. This paper presents a novel approach to classifying chest x-rays on the basis of the practical disease condition. Harnessing the potential features of contentbased image retrieval, the proposed system introduces a novel concept of attribute map that not only performs comprehensive classification but also makes the complete computational model extremely lightweight. The study outcome proved to offer better accuracy with the proposed non-iterative process in contrast to existing classifier design.

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Deep-learning: A potential method for tuberculosis detection using chest radiography

Cited by 95 publications

References 14 publications

Image Enhancement for Tuberculosis Detection Using Deep Learning

Image Enhancement for Tuberculosis Detection Using Deep Learning

Ensemble deep learning for tuberculosis detection

Comprehensive Classification Model for Diagnosing Multiple Disease Condition from Chest X-Ray

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