Application of a Computer-aid Diagnosis of Pneumoconiosis for CR X-ray Images

Abe, Kôji; Minami, Masaki; Miyazaki, Ryosuke; Tian, Huayu

doi:10.14738/jbemi.15.606

Cited by 4 publications

(5 citation statements)

References 7 publications

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“…The study found five major categories of texture feature analysis methods, where texture feature was extracted using Fourier spectrum [ 39 , 40 , 44 , 48 , 50 , 53 , 58 ], co-occurrence matrix analysis [ 42 , 48 , 50 , 53 , 55 , 57 , 58 , 59 , 61 , 64 , 79 ], histogram analysis [ 34 , 47 , 50 , 55 , 59 , 61 , 63 , 66 ], wavelet transform [ 52 , 56 ], and density distribution [ 42 , 45 , 46 , 51 , 54 , 60 , 62 ]. The details of the five methods are discussed in the following subsections and the texture features extracted from them, as described in Table 1 , are summarised.…”

Section: Analysis Of Returned Articlesmentioning

confidence: 99%

“…An image’s texture varies depending on the scanner’s quality, which is very expensive in the clinical diagnosis system. To address this issue, Abe et al [ 51 , 54 ] and Nakamura et al [ 60 , 62 ] proposed a charge-coupled device (CCD) scanner for CAD of pneumoconiosis in CXR, where they computed the feature characteristics based on density distribution in a particular region or the areas between the ribs and its inter-costal. Their descriptions can be found in Table 1 .…”

Section: Analysis Of Returned Articlesmentioning

confidence: 99%

“…Most texture features, from Fourier spectrum, co-occurrence matrix, histogram, wavelet transform, and density distribution, are classified using different traditional machine learning classifiers, namely support vector machines (SVM) [ 51 , 52 , 54 , 56 , 57 , 59 , 60 , 61 , 62 , 63 ], decision trees (DT) [ 52 , 56 ], random trees (RT) [ 51 , 54 , 60 , 62 ], artificial neural networks (ANNs) [ 53 , 55 , 58 ], K-nearest neighbors (KNN) [ 65 ], self-organizing maps (SOM) [ 65 ], backpropagation (BP), radial basis function (RBF) neural networks (NN) [ 51 , 54 , 60 , 62 , 65 , 66 ], and ensemble classifiers [ 52 , 59 , 61 ]. Figure 5 shows how the researchers connected various texture features with traditional machine learning classifiers to detect CWP in CXR.…”

Section: Analysis Of Returned Articlesmentioning

confidence: 99%

See 2 more Smart Citations

Computer-Aided Diagnosis of Coal Workers’ Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review

Devnath

Summons

Luo

et al. 2022

IJERPH

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Computer-aided diagnostic (CAD) systems can assist radiologists in detecting coal workers’ pneumoconiosis (CWP) in their chest X-rays. Early diagnosis of the CWP can significantly improve workers’ survival rate. The development of the CAD systems will reduce risk in the workplace and improve the quality of chest screening for CWP diseases. This systematic literature review (SLR) amis to categorise and summarise the feature extraction and detection approaches of computer-based analysis in CWP using chest X-ray radiographs (CXR). We conducted the SLR method through 11 databases that focus on science, engineering, medicine, health, and clinical studies. The proposed SLR identified and compared 40 articles from the last 5 decades, covering three main categories of computer-based CWP detection: classical handcrafted features-based image analysis, traditional machine learning, and deep learning-based methods. Limitations of this review and future improvement of the review are also discussed.

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Section: Analysis Of Returned Articlesmentioning

confidence: 99%

Section: Analysis Of Returned Articlesmentioning

confidence: 99%

Section: Analysis Of Returned Articlesmentioning

confidence: 99%

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Computer-Aided Diagnosis of Coal Workers’ Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review

Devnath

Summons

Luo

et al. 2022

IJERPH

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“…Handcrafted features like texture [ 30 , 32 ] were extracted from the left–right lung zones [ 40 , 41 , 42 , 43 ]. Following feature selection, they were fed into various machine learning classifiers, including support vector machines (SVM) [ 40 , 41 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ], decision trees (DT) [ 47 , 48 ], random trees (RT) [ 44 , 49 , 50 , 51 ], artificial neural networks (ANNs) [ 52 , 53 , 54 ], K-nearest neighbours (KNN) [ 55 ], self-organizing map (SOM) [ 55 ], backpropagation (BP), the radial basis function (RBF) neural networks (NN) [ 44 , 49 , 50 , 51 , 55 , 56 ], and Ensemble classifier [ 41 , 43 , 48 ]. Among the classifiers, SVM had the best overall detection accuracy, with a 73.17 percent success rate when using the same dataset as this study.…”

Section: Research Backgroundmentioning

confidence: 99%

Detection and Visualisation of Pneumoconiosis Using an Ensemble of Multi-Dimensional Deep Features Learned from Chest X-rays

Devnath

Fan

Luo

et al. 2022

IJERPH

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Pneumoconiosis is a group of occupational lung diseases induced by mineral dust inhalation and subsequent lung tissue reactions. It can eventually cause irreparable lung damage, as well as gradual and permanent physical impairments. It has affected millions of workers in hazardous industries throughout the world, and it is a leading cause of occupational death. It is difficult to diagnose early pneumoconiosis because of the low sensitivity of chest radiographs, the wide variation in interpretation between and among readers, and the scarcity of B-readers, which all add to the difficulty in diagnosing these occupational illnesses. In recent years, deep machine learning algorithms have been extremely successful at classifying and localising abnormality of medical images. In this study, we proposed an ensemble learning approach to improve pneumoconiosis detection in chest X-rays (CXRs) using nine machine learning classifiers and multi-dimensional deep features extracted using CheXNet-121 architecture. There were eight evaluation metrics utilised for each high-level feature set of the associated cross-validation datasets in order to compare the ensemble performance and state-of-the-art techniques from the literature that used the same cross-validation datasets. It is observed that integrated ensemble learning exhibits promising results (92.68% accuracy, 85.66% Matthews correlation coefficient (MCC), and 0.9302 area under the precision–recall (PR) curve, compared to individual CheXNet-121 and other state-of-the-art techniques. Finally, Grad-CAM was used to visualise the learned behaviour of individual dense blocks within CheXNet-121 and their ensembles into three-color channels of CXRs. We compared the Grad-CAM-indicated ROI to the ground-truth ROI using the intersection of the union (IOU) and average-precision (AP) values for each classifier and their ensemble. Through the visualisation of the Grad-CAM within the blue channel, the average IOU passed more than 90% of the pneumoconiosis detection in chest radiographs.

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“…Recent advances in machine learning and AI have led to novel approaches in X-ray radiodiagnosis which are primarily focused on optimized versions of convolutional neural networks (CNNs) (Akhter et al, 2023). The first automatic system based on Haralick features used digitized lung X-rays and identified black lung disease with 96% accuracy (Kruger et al, 1974;Abe et al, 2014), while physician accuracy varies from 86% to 100% (Hall et al, 1975). Although the nomenclature was not yet standardized at the time of the first large-scale pneumoconiosis study (Kruger et al, 1974) using Haralick features (Haralick et al, 1973;Haralick, 1979), the five features used for patient classification were "autocorrelation" (similar to the normalized correlation feature f 3 from Eq.…”

Section: Introductionmentioning

confidence: 99%

Bounds for Haralick features in synthetic images with sinusoidal gradients

Oprisan,

Oprisan

2023

Front. Signal Process.

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Introduction: The gray-level co-occurrence matrix (GLCM) reduces the dimension of an image to a square matrix determined by the number of gray-level intensities present in that image. Since GLCM only measures the co-occurrence frequency of pairs of gray levels at a given distance from each other, it also stores information regarding the gradients of gray-level intensities in the original image.Methods: The GLCM is a second-order statical method of encoding image information and dimensionality reduction. Image features are scalars that reduce GLCM dimensionality and allow fast texture classification. We used Haralick features to extract information regarding image gradients based on the GLCM.Results: We demonstrate that a gradient of k gray levels per pixel in an image generates GLCM entries on the kth parallel line to the main diagonal. We find that, for synthetic sinusoidal periodic gradients with different wavelengths, the number of gray levels due to intensity quantization follows a power law that also transpires in some Haralick features. We estimate bounds for four of the most often used Haralick features: energy, contrast, correlation, and entropy. We find good agreement between our analytically predicted values of Haralick features and the numerical results from synthetic images of sinusoidal periodic gradients.Discussion: This study opens the possibility of deriving bounds for Haralick features for targeted textures and provides a better selection mechanism for optimal features in texture analysis applications.

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Application of a Computer-aid Diagnosis of Pneumoconiosis for CR X-ray Images

Abstract: This paper presents a method for applying a computer-aided diagnosis (CAD) for pneumoconiosis to chest X-ray images digitalized by the computed radiography (CR) system. When we reported the CAD before, we showed performance of the CAD for chest X-ray images digitalized by a CCD scanner.

Cited by 4 publications

References 7 publications

Computer-Aided Diagnosis of Coal Workers’ Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review

Computer-Aided Diagnosis of Coal Workers’ Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review

Detection and Visualisation of Pneumoconiosis Using an Ensemble of Multi-Dimensional Deep Features Learned from Chest X-rays

Bounds for Haralick features in synthetic images with sinusoidal gradients

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