A discrimination method for the detection of pneumonia using chest radiograph

Noor, Norliza Mohd; Rijal, Omar Mohd; Ashari, Yunus; Abu-Bakar, S. A. R.

doi:10.1016/j.compmedimag.2009.08.005

Cited by 18 publications

(6 citation statements)

References 12 publications

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“…A comparison of the use of PC and that of our earlier work [ 9 ] showed that Type I Errors and Type II Errors were much less using PC when energy, contrast, and homogeneity were used as features for discrimination.…”

Section: Resultsmentioning

confidence: 93%

“…However problems arise with the use of X-rays where studies have shown that the accuracy of the X-ray interpretation is subject to varying degrees of observer error [ 8 ]. Current methods or algorithms for disease detection mainly focus on the discrimination between normal images and images with signs of disease involving chest radiograph [ 9 – 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…There are many techniques used in texture feature extraction. A popular technique is the use of wavelet textures; for example, in [ 9 ], Daubechies wavelet was applied. However, Daubechies wavelet does not preserve phase information [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of Phase Congruency for Discriminating Some Lung Diseases Using Chest Radiograph

Rijal

Ebrahimian

Noor

et al. 2015

Computational and Mathematical Methods in Medicine

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A novel procedure using phase congruency is proposed for discriminating some lung disease using chest radiograph. Phase congruency provides information about transitions between adjacent pixels. Abrupt changes of phase congruency values between pixels may suggest a possible boundary or another feature that may be used for discrimination. This property of phase congruency may have potential for deciding between disease present and disease absent where the regions of infection on the images have no obvious shape, size, or configuration. Five texture measures calculated from phase congruency and Gabor were shown to be normally distributed. This gave good indicators of discrimination errors in the form of the probability of Type I Error (δ) and the probability of Type II Error (β). However, since 1 − δ is the true positive fraction (TPF) and β is the false positive fraction (FPF), an ROC analysis was used to decide on the choice of texture measures. Given that features are normally distributed, for the discrimination between disease present and disease absent, energy, contrast, and homogeneity from phase congruency gave better results compared to those using Gabor. Similarly, for the more difficult problem of discriminating lobar pneumonia and lung cancer, entropy and homogeneity from phase congruency gave better results relative to Gabor.

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Section: Resultsmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

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Application of Phase Congruency for Discriminating Some Lung Diseases Using Chest Radiograph

Rijal

Ebrahimian

Noor

et al. 2015

Computational and Mathematical Methods in Medicine

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“…The modified principal component (ModPC) method was introduced in (Noor et al 2010) where PNEU is discriminated from normals. The ModPC method is now extended for pair-wise comparison between three types of diseases namely, PNEU, PTB, LC, and normals.…”

Section: Modified Principal Component Methodsmentioning

confidence: 99%

“…In either approach, the appropriate Q matrix is first derived before the principal component (PC) is applied onto it. This method named as the modified PC (modPC) method was successfully applied in the discrimination between PNEU and NL (Noor et al 2010).…”

Section: Discrimination Between Disease Present and Disease Absent (Nl)mentioning

confidence: 99%

Texture-Based Statistical Detection and Discrimination of Some Respiratory Diseases Using Chest Radiograph

Noor

Rijal

Ashari

et al. 2014

Lecture Notes in Bioengineering

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This chapter proposes a novel texture-based statistical procedure to detect and discriminate lobar pneumonia, pulmonary tuberculosis (PTB), and lung cancer simultaneously using digitized chest radiographs. A modified principal component method applied to wavelet texture measures yielded feature vectors for the statistical discrimination procedure. The procedure initially discriminated between a particular disease and the normals. The maximum column sum energy texture measure yielded 98 % correct classification rates for all three diseases. The diseases were then compared pair-wise, and the combination of mean of energy and maximum value texture measures gave correct classification rates of 70, 97, and 79 % for pneumonia, PTB, and lung cancer, respectively.

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Transfer Learning Based Approach for Pneumonia Detection Using Customized VGG16 Deep Learning Model

Ranjan

Kumar

Gupta

et al. 2022

Internet of Things and Connected Technologies

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A discrimination method for the detection of pneumonia using chest radiograph

Cited by 18 publications

References 12 publications

Application of Phase Congruency for Discriminating Some Lung Diseases Using Chest Radiograph

Application of Phase Congruency for Discriminating Some Lung Diseases Using Chest Radiograph

Texture-Based Statistical Detection and Discrimination of Some Respiratory Diseases Using Chest Radiograph

Transfer Learning Based Approach for Pneumonia Detection Using Customized VGG16 Deep Learning Model

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