Pneumonia Detection on Chest X-Ray Using Machine Learning Paradigm

Chandra, Tej Bahadur; Verma, Kesari

doi:10.1007/978-981-32-9088-4_3

Cited by 78 publications

(61 citation statements)

References 14 publications

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“…Chest X-Ray (CXR) is one of the important, non-invasive clinical adjuncts that play an essential role in the preliminary investigation of different pulmonary abnormalities ( Chandra & Verma, 2020 , Chandra and Verma, 2020a , Chandra et al, 2020 , Ke et al, 2019 ). It can act as an alternative screening modality for the detection of nCOVID-19 or to validate the related diagnosis, where the CXR images are interpreted by expert radiologists to look for infectious lesions associated with nCOVID-19.…”

Section: Introductionmentioning

confidence: 99%

Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble

Chandra

Verma

Singh

et al. 2021

Expert Systems with Applications

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235

190

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Highlights Proposed automatic COVID screening (ACoS) system for detection of infected patients. Random image augmentation is applied to incorporate the variability in the images. Applied hierarchical (two phase) classification to segregate three classes. Majority vote based classifier ensemble is used to combine model’s prediction. Proposed method show promising potential to detect nCOVID-19 infected patients.

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Section: Introductionmentioning

confidence: 99%

Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble

Chandra

Verma

Singh

et al. 2021

Expert Systems with Applications

Self Cite

235

190

View full text Add to dashboard Cite

show abstract

“…A wide array of research works uncovers the discriminatory information that best expresses pneumonia from normal samples on chest X-rays. The methods employed in the research of pneumonia/COVID-19 classification from chest X-rays fall into these categories: Machine learning (ML) methods [11] , [12] , [13] , statistical approaches [14] , CNN architectures [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , transfer learning [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , complex CNN models [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] and adversarial networks [43] .…”

Section: Related Workmentioning

confidence: 99%

“…Chandra et al. propose a three-step solution for automatic detection of pneumonia [11] . Regions of the X-rays enclosing the lungs are extracted and quantified using first-order statistical features like mean, kurtosis, etc.…”

Section: Related Workmentioning

confidence: 99%

Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN

Karthik

Menaka

Hariharan

2021

Applied Soft Computing

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COVID-19 is a deadly viral infection that has brought a significant threat to human lives. Automatic diagnosis of COVID-19 from medical imaging enables precise medication, helps to control community outbreak, and reinforces coronavirus testing methods in place. While there exist several challenges in manually inferring traces of this viral infection from X-ray, Convolutional Neural Network (CNN) can mine data patterns that capture subtle distinctions between infected and normal X-rays. To enable automated learning of such latent features, a custom CNN architecture has been proposed in this research. It learns unique convolutional filter patterns for each kind of pneumonia. This is achieved by restricting certain filters in a convolutional layer to maximally respond only to a particular class of pneumonia/COVID-19. The CNN architecture integrates different convolution types to aid better context for learning robust features and strengthen gradient flow between layers. The proposed work also visualizes regions of saliency on the X-ray that have had the most influence on CNN’s prediction outcome. To the best of our knowledge, this is the first attempt in deep learning to learn custom filters within a single convolutional layer for identifying specific pneumonia classes. Experimental results demonstrate that the proposed work has significant potential in augmenting current testing methods for COVID-19. It achieves an F1-score of 97.20% and an accuracy of 99.80% on the COVID-19 X-ray set.

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“…For instance, Sousa et al [8] extracted wavelet features from CXRs and used as input to three different machine learning methods. Chandra et al [9] extracted histogram features and used five different image classifiers. However, they used hand-crafted image features, which is a time-consuming and labor-intensive task.…”

Section: Introductionmentioning

confidence: 99%

Multi-View Ensemble Convolutional Neural Network to Improve Classification of Pneumonia in Low Contrast Chest X-Ray Images

Ferreira

Cárdenas

Moreno

et al. 2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Pneumonia is one of the leading causes of childhood mortality worldwide. Chest x-ray (CXR) can aid the diagnosis of pneumonia, but in the case of low contrast images, it is important to include computational tools to aid specialists. Deep learning is an alternative because it can identify patterns automatically, even in low-resolution images. We propose herein a convolutional neural network (CNN) architecture with different training strategies towards detecting pneumonia on CXRs and distinguishing its subforms of bacteria and virus. We also evaluated different image pre-processing methods to improve the classification. This study used CXRs from pediatric patients from a public pneumonia CXR dataset. The pre-processing methods evaluated were image cropping and histogram equalization. To classify the images, we adopted the VGG16 CNN and replaced its fully-connected layers with a customized multilayer perceptron. With this architecture, we proposed and evaluated four different training strategies: original CXR image (baseline), chest-cavity-cropped image (A), and histogram-equalized segmented image (B). The last strategy method (C) implemented is based on ensemble between strategies A and B. The performance was assessed by the area under the ROC curve (AUC) with 95% confidence interval (CI), accuracy, sensitivity, specificity, and F1-score. The ensemble model C yielded the highest performances: AUC of 0.97 (CI: 0.96-0.99) to classify pneumonia vs. normal, and AUC of 0.91 (CI: 0.88-0.94) to classify bacterial vs. viral cases. All models that used pre-processed images showed higher AUC than baseline, which used the original CXR image. Image cropping and histogram equalization reduced irrelevant information from the exam, enhanced contrast, and was able to identify fine CXR texture details. The proposed ensemble model increased the representation of inflammatory patterns from bacteria and viruses with few epochs to train the deep CNNs. Clinical relevance-Deep learning can identify complex radiographic patterns in low contrast images due to pneumonia and distinguish its subforms of bacteria and virus. The correlation of imaging with lab results could accelerate the adoption of complementary exams to confirm the disease's cause.

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Pneumonia Detection on Chest X-Ray Using Machine Learning Paradigm

Cited by 78 publications

References 14 publications

Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble

Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble

Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN

Multi-View Ensemble Convolutional Neural Network to Improve Classification of Pneumonia in Low Contrast Chest X-Ray Images

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