Advanced approaches to computer-aided detection of thoracic diseases on chest X-rays

Zakirov, Adel; Kuleev, Ramil; Vladimirov, Anatoli

doi:10.12988/ams.2015.54348

Cited by 18 publications

(14 citation statements)

References 23 publications

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“…Many commercial products have been developed for the clinical applications, including CAD4 TB, Riverain, and Delft imaging systems [3]. However, because of the complexity of the chest X-rays, the automatic detection of the diseases remains unresolved, and most of the existing CAD systems are aimed at the early detection of the lung cancer.…”

Section: Introductionmentioning

confidence: 99%

Computer-aided detection in chest radiography based on artificial intelligence: a survey

et al. 2018

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As the most common examination tool in medical practice, chest radiography has important clinical value in the diagnosis of disease. Thus, the automatic detection of chest disease based on chest radiography has become one of the hot topics in medical imaging research. Based on the clinical applications, the study conducts a comprehensive survey on computer-aided detection (CAD) systems, and especially focuses on the artificial intelligence technology applied in chest radiography. The paper presents several common chest X-ray datasets and briefly introduces general image preprocessing procedures, such as contrast enhancement and segmentation, and bone suppression techniques that are applied to chest radiography. Then, the CAD system in the detection of specific disease (pulmonary nodules, tuberculosis, and interstitial lung diseases) and multiple diseases is described, focusing on the basic principles of the algorithm, the data used in the study, the evaluation measures, and the results. Finally, the paper summarizes the CAD system in chest radiography based on artificial intelligence and discusses the existing problems and trends.

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Section: Introductionmentioning

confidence: 99%

Computer-aided detection in chest radiography based on artificial intelligence: a survey

et al. 2018

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“…Automatic lung nodule detection on CXR dates back several decades. Various traditional CAD systems are currently available but these have had limited success 10 . Unlike DNNs, existing conventional CAD systems employ more traditional image analysis techniques and need to perform many processing steps including image pre‐processing, lung field and other anatomical segmentation, and feature analysis.…”

Section: Automatic Disease Detection On Cxr Imagesmentioning

confidence: 99%

Deep learning applied to automatic disease detection using chest X‐rays

Moses

2021

J Med Imag Rad Onc

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Deep learning (DL) has shown rapid advancement and considerable promise when applied to the automatic detection of diseases using CXRs. This is important given the widespread use of CXRs across the world in diagnosing significant pathologies, and the lack of trained radiologists to report them. This review article introduces the basic concepts of DL as applied to CXR image analysis including basic deep neural network (DNN) structure, the use of transfer learning and the application of data augmentation. It then reviews the current literature on how DNN models have been applied to the detection of common CXR abnormalities (e.g. lung nodules, pneumonia, tuberculosis and pneumothorax) over the last few years. This includes DL approaches employed for the classification of multiple different diseases (multi‐class classification). Performance of different techniques and models and their comparison with human observers are presented. Some of the challenges facing DNN models, including their future implementation and relationships to radiologists, are also discussed.

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“…By means of some standardized procedure of X-ray photography followed by image preprocessing (see [11]), we have got a digital image: a matrix M sized n × n, n ∈ N (in our case n = 512), with each element z pq ∈ M being a function of color of the corresponding pixel of the X-ray. In our case z pq ∈ [0, 1] which corresponds to gray scale (black color for dense structures (bones, blood vessels, and pathological formations), white color for thinner matter (lungs and cavities in them)).…”

Section: Multifractal Parametrizationmentioning

confidence: 99%