Uniformizing Techniques to Process CT Scans with 3D CNNs for Tuberculosis Prediction

Zunair, Hasib; Rahman, Asifur; Mohammed, Nabeel; Cohen, Joseph

doi:10.1007/978-3-030-59354-4_15

Cited by 78 publications

(65 citation statements)

References 17 publications

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“…Zunair et al [47] Feature extraction reduces the characteristic image values, and results are obtained easier. The extraction function is a particular format for reducing pattern recognition dimensionality and other fields of imaging science.…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

Analysis of Deep Learning Techniques for Tuberculosis Disease

2021

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The airborne disease is a severe disease in the world that spreads exponentially. An Immunochromatography Test(IT) and Biological Aerosol Particles (BAP) test with a disposable instrument that will be recorded in the literature is a standard diagnostic procedure for respiratory infections, such as influenza and TB, in the investigation causes of suspected disease. This examination helps the examiner to identify infectious patients quickly, efficiently, and inexpensively. Self-diagnosis, however, is problematic because it requires a medical specialist to diagnose the diseases in infections. Chest radiography is also not only the primary screening method that is focused on surgical imaging but also diagnostic radiology. Various methods of classification of Tuberculosis are analyzed here. Computer-Assisted Diagnosis (CAD) has become popular, and many researchers are involved in these fields of study. Numerous methods have been suggested for airborne identification and Classification of lung diseases. The medical history of airborne tuberculosis disease in chest X-rays and a review of the different approaches to diagnosing and Classification of the airborne disease are discussed in this article. According to the literature on the relevant policies, items in this research field have been surveyed until 2020.

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Section: Feature Extraction and Selectionmentioning

confidence: 99%

Analysis of Deep Learning Techniques for Tuberculosis Disease

2021

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“…CT images [28,17] have also been widely used for automatic disease classification, in part because analysis of 3D imaging offers additional context [40]. In particular, the value of CT scans for tuberculosis (TB) classification using neural networks has been recently highlighted [41,15]. Both CT and X-ray can be used for diagnosis and analysis of TB, but CT may offer additional visualization and insight [24,3,1].…”

Section: Related Work Automatic Disease Classificationmentioning

confidence: 99%

“…For CT and CT+X-ray experiments with two classes, we relied on the 3D convolutional architecture proposed by [41] with Adam optimizer, learning rate 0.0001 and batch size 20. The network was trained until convergence with binary cross-entropy (BCE) loss for 2-class experiments.…”

Section: Tb Analysis Networkmentioning

confidence: 99%

Improving Tuberculosis (TB) Prediction using Synthetically Generated Computed Tomography (CT) Images

Lewis¹,

Mahmoodi²,

Zhou³

et al. 2021

Preprint

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The evaluation of infectious disease processes on radiologic images is an important and challenging task in medical image analysis. Pulmonary infections can often be best imaged and evaluated through computed tomography (CT) scans, which are often not available in low-resource environments and difficult to obtain for critically ill patients. On the other hand, X-ray, a different type of imaging procedure, is inexpensive, often available at the bedside and more widely available, but offers a simpler, two dimensional image. We show that by relying on a model that learns to generate CT images from X-rays synthetically, we can improve the automatic disease classification accuracy and provide clinicians with a different look at the pulmonary disease process. Specifically, we investigate Tuberculosis (TB), a deadly bacterial infectious disease that predominantly affects the lungs, but also other organ systems. We show that relying on synthetically generated CT improves TB identification by 7.50% and distinguishes TB properties up to 12.16% better than the X-ray baseline.

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“…This approach involves thorough image processing, subsequently performing feature extraction using TensorFlow and 3D CNN to further augment the metadata with the features extracted from the image data, and finally perform six class binary classification using the random forest. Another attempt for this problem was proposed by Zunair et al [ 87 ]. They proposed a 16-layer 3D convolutional neural network with a slice selection.…”

Section: The Taxonomy Of State-of-the-art Work On Lung Disease Detection Using Deep Learningmentioning

confidence: 99%

A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions

et al. 2020

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The recent developments of deep learning support the identification and classification of lung diseases in medical images. Hence, numerous work on the detection of lung disease using deep learning can be found in the literature. This paper presents a survey of deep learning for lung disease detection in medical images. There has only been one survey paper published in the last five years regarding deep learning directed at lung diseases detection. However, their survey is lacking in the presentation of taxonomy and analysis of the trend of recent work. The objectives of this paper are to present a taxonomy of the state-of-the-art deep learning based lung disease detection systems, visualise the trends of recent work on the domain and identify the remaining issues and potential future directions in this domain. Ninety-eight articles published from 2016 to 2020 were considered in this survey. The taxonomy consists of seven attributes that are common in the surveyed articles: image types, features, data augmentation, types of deep learning algorithms, transfer learning, the ensemble of classifiers and types of lung diseases. The presented taxonomy could be used by other researchers to plan their research contributions and activities. The potential future direction suggested could further improve the efficiency and increase the number of deep learning aided lung disease detection applications.

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Uniformizing Techniques to Process CT Scans with 3D CNNs for Tuberculosis Prediction

Cited by 78 publications

References 17 publications

Analysis of Deep Learning Techniques for Tuberculosis Disease

Analysis of Deep Learning Techniques for Tuberculosis Disease

Improving Tuberculosis (TB) Prediction using Synthetically Generated Computed Tomography (CT) Images

A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions

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