Analysis of Tuberculosis in Chest Radiographs for Computerized Diagnosis using Bag of Keypoint Features

Govindarajan, S.; Swaminathan, Ramakrishnan

doi:10.1007/s10916-019-1222-8

Cited by 33 publications

(35 citation statements)

References 33 publications

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“…Network training is carried out by examining the effect of filter sizes on classification accuracy for different learning rates [0.001, 0.01] at different number of CNN layers [3,5,7]. Mini-batch samples and maximum epochs are selected empirically as 64 and 30 respectively with data shuffling at every epoch [9,31].…”

Section: Model Evaluationmentioning

confidence: 99%

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Differentiation of COVID-19 conditions in planar chest radiographs using optimized convolutional neural networks

Govindarajan

Swaminathan

2020

Appl Intell

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In this study, an attempt has been made to differentiate Novel Coronavirus-2019 (COVID-19) conditions from healthy subjects in Chest radiographs using a simplified end-to-end Convolutional Neural Network (CNN) model and occlusion sensitivity maps. Early detection and faster automated screening of the COVID-19 patients is essential. For this, the images are considered from publicly available datasets. Significant biomarkers representing critical image features are extracted from CNN by experimentally investigating on cross-validation methods and hyperparameter settings. The performance of the network is evaluated using standard metrics. Perturbation based occlusion sensitivity maps are employed on the features obtained from the classification model to visualise the localization of abnormal areas. Results demonstrate that the simplified CNN model with optimised parameters is able to extract significant features with a sensitivity of 97.35% and F-measure of 96.71% to detect COVID-19 images. The algorithm achieves an Area Under the Curve-Receiver Operating Characteristic score of 99.4% with Matthews correlation coefficient of 0.93. High value of Diagnostic odds ratio is also obtained. Occlusion sensitivity maps provide precise localization of abnormal regions by identifying COVID-19 conditions. As early detection through chest radiographic images are useful for automated screening of the disease, this method appears to be clinically relevant in providing a visual diagnostic solution using a simplified and efficient model.

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Section: Model Evaluationmentioning

confidence: 99%

“…Chest Radiographic (CXR) imaging is considered as the initial diagnostic modality and primary screening tool to detect several respiratory pathologies such as Tuberculosis (TB) and Pneumonia [7,8]. This modality is reliable and portable by avoiding risk of any disease exposure especially in remote settings [8].…”

Section: Introductionmentioning

confidence: 99%

Differentiation of COVID-19 conditions in planar chest radiographs using optimized convolutional neural networks

Govindarajan

Swaminathan

2020

Appl Intell

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“…They implemented five ROI localization methods to find the best performing model. Govindarajan et al [27] proposed a TB classification scheme using 'Speeded Up Robust Feature' (SURF) descriptor and 'Bag of Features' approach. Distance regularized level set was used to segment the lung field and Multilayer perceptron was used to classify normal and TB infected images.…”

Section: Introductionmentioning

confidence: 99%

Ensemble learning based automatic detection of tuberculosis in chest X-ray images using hybrid feature descriptors

Shaukat²,

2021

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Tuberculosis (TB) remains one of the major health problems in modern times with a high mortality rate. While efforts are being made to make early diagnosis accessible and more reliable in high burden TB countries, digital chest radiography has become a popular source for this purpose. However, the screening process requires expert radiologists which may be a potential barrier in developing countries. A fully automatic computer-aided diagnosis system can reduce the need of trained personnel for early diagnosis of TB using chest X-ray images. In this paper, we have proposed a novel TB detection technique that combines hand-crafted features with deep features (convolutional neural network-based) through Ensemble Learning. Handcrafted features were extracted via Gabor Filter and deep features were extracted via pre-trained deep learning models. Two publicly available datasets namely (i) Montgomery and (ii) Shenzhen were used to evaluate the proposed system. The proposed methodology was validated with a k-fold cross-validation scheme. The area under receiver operating characteristics curves of 0.99 and 0.97 were achieved for Shenzhen and Montgomery datasets respectively which shows the superiority of the proposed scheme.

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Section: Introductionmentioning

confidence: 99%

“…Ginneken et al [ 10 ] aimed to detect signs of diffuse texture abnormalities in chest radiographs to determine chest radiograph abnormalities, which is similar to [ 11 ]. Govindarajan et al [ 12 ] used Bag of Features approach with Speeded-Up Robust Feature descriptor to classify TB CXRs. These existing algorithms use well-designed morphological features to improve screening performance, and the main process includes image preprocessing, feature extraction, and feature classification.…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided System for the Detection of Multicategory Pulmonary Tuberculosis in Radiographs

Xie

Han

et al. 2020

Journal of Healthcare Engineering

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The early screening and diagnosis of tuberculosis plays an important role in the control and treatment of tuberculosis infections. In this paper, an integrated computer-aided system based on deep learning is proposed for the detection of multiple categories of tuberculosis lesions in chest radiographs. In this system, the fully convolutional neural network method is used to segment the lung area from the entire chest radiograph for pulmonary tuberculosis detection. Different from the previous analysis of the whole chest radiograph, we focus on the specific tuberculosis lesion areas for the analysis and propose the first multicategory tuberculosis lesion detection method. In it, a learning scalable pyramid structure is introduced into the Faster Region-based Convolutional Network (Faster RCNN), which effectively improves the detection of small-area lesions, mines indistinguishable samples during the training process, and uses reinforcement learning to reduce the detection of false-positive lesions. To compare our method with the current tuberculosis detection system, we propose a classification rule for whole chest X-rays using a multicategory tuberculosis lesion detection model and achieve good performance on two public datasets (Montgomery: AUC = 0.977 and accuracy = 0.926; Shenzhen: AUC = 0.941 and accuracy = 0.902). Our proposed computer-aided system is superior to current systems that can be used to assist radiologists in diagnoses and public health providers in screening for tuberculosis in areas where tuberculosis is endemic.

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Analysis of Tuberculosis in Chest Radiographs for Computerized Diagnosis using Bag of Keypoint Features

Cited by 33 publications

References 33 publications

Differentiation of COVID-19 conditions in planar chest radiographs using optimized convolutional neural networks

Differentiation of COVID-19 conditions in planar chest radiographs using optimized convolutional neural networks

Ensemble learning based automatic detection of tuberculosis in chest X-ray images using hybrid feature descriptors

Computer-Aided System for the Detection of Multicategory Pulmonary Tuberculosis in Radiographs

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