A multiresolution support vector machine based algorithm for pneumoconiosis detection from chest radiographs

Sundararajan, Ramasubramanian; Xu, Hao; Annangi, Pavan; Tao, Xu; Sun, Xiaochun; Mao, Ling

doi:10.1109/isbi.2010.5490239

Cited by 15 publications

(14 citation statements)

References 9 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%

“…In the last few decades, these features have hardly been used in many CAD systems. This review also noticed that in the CAD of pneumoconiosis, texture features were extracted using grey-level co-occurrence matrix, grey-level co-occurrence histograms and spatial dependence matrix [ 42 , 48 , 50 , 53 , 55 , 58 , 59 , 61 , 64 , 65 ]. This study observed that texture features, correlation, contrast, homogeneity, entropy, and energy were mainly used to detect CWP in the chest X-ray radiographs (CXR).…”

Section: Analysis Of Returned Articlesmentioning

confidence: 99%

“…The tonal distribution indicates the variation of colours, especially grey-level intensity distribution, measured from histogram analysis [ 86 , 87 ]. In CAD of CWP diseases, several researchers have proposed extracting texture features using histogram analysis [ 34 , 47 , 50 , 55 , 59 , 61 , 63 , 66 ]. Most of them were computed using a set of common features, namely, mean, variance, skewness, kurtosis, energy, and entropy, from the grey-level intensity distribution of ROIs images.…”

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%

See 1 more Smart Citation

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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“…Sundararajan et.al. (2010) [4] developed a support vector machine for the detection process of pneumoconiosis. Pneumoconiosis is an occupational and a restrictive lung disease caused by the inhalation of dust, in mines and from agriculture.…”

Section: Review Of Literaturementioning

confidence: 99%

Lung cancer detection and classification on CT scan images using enhanced artificial bee colony optimization

Perumal¹,

Velmurugan²

2018

IJET

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In recent years, prediction of cancer at earlier stages is obligatory to increase the chance of survival of the afflicted. The most dreadful type is lung cancer, which is identified as one of the most common diseases among humans worldwide. In this research work, the raw input image which usually suffers from noise issues are highly enhanced using Gabor filter image processing. The region of interest from lung cancer images are extracted with Otsu’s threshold segmentation method and 5- level HAAR discrete wavelet transform method which possess maximum speed and high accuracy. The proposed Enhanced Artificial Bee Colony Optimization (EABC) is applied to detect the cancer suspected area in CT (Computed tomography) scan images. The proposed EABC implementation part, utilizes CT (Computed Tomography) scanned lung images with MATLAB software environment. This method can assist radiologists and medicinal experts to recognize the illness of syndromes at primary stages and to evade severe advance stages of cancer.

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“…They conducted tests on patients with TB and ILD. Sundararajan et al [2010] targeted the detection of pneumoconiosis. They used various textural features and separate SVM classifiers for disjoint segments of the lung, while a single classifier to label the image as a whole.…”

Section: Introductionmentioning

confidence: 99%

Algorithm fusion to improve detection of lung cancer on chest radiographs

Orbán

Horväth²

2012

International Journal of Intelligent Computing and Cybernetics

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Purpose -The purpose of this paper is to show an efficient method for the detection of signs of early lung cancer. Various image processing algorithms are presented for different types of lesions, and a scheme is proposed for the combination of results. Design/methodology/approach -A computer aided detection (CAD) scheme was developed for detection of lung cancer. It enables different lesion enhancer algorithms, sensitive to specific lesion subtypes, to be used simultaneously. Three image processing algorithms are presented for the detection of small nodules, large ones, and infiltrated areas. The outputs are merged, the false detection rate is reduced with four separated support vector machine (SVM) classifiers. The classifier input comes from a feature selection algorithm selecting from various textural and geometric features. A total of 761 images were used for testing, including the database of the Japanese Society of Radiological Technology (JSRT). Findings -The fusion of algorithms reduced false positives on average by 0.6 per image, while the sensitivity remained 80%. On the JSRT database the system managed to find 60.2% of lesions at an average of 2.0 false positives per image. The effect of using different result evaluation criteria was tested and a difference as high as 4 percentage points in sensitivity was measured. The system was compared to other published methods.Originality/value -This study proves the usefulness of lesion enhancement decomposition, while proposing a scheme for the fusion of algorithms. Furthermore, a new algorithm is introduced for the detection of infiltrated areas, possible signs of lung cancer, neglected by previous solutions. * Marczibányi tér 5.,

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A multiresolution support vector machine based algorithm for pneumoconiosis detection from chest radiographs

Cited by 15 publications

References 9 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

Lung cancer detection and classification on CT scan images using enhanced artificial bee colony optimization

Algorithm fusion to improve detection of lung cancer on chest radiographs

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