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Article ; Online: Diagnostics of Multi Drug Resistant Tuberculosis in Chest Radiographs using Local Textures & Extreme Gradient Boosting

Govindarajan Satyavratan / Manuskandan S. R. / Swaminathan Ramakrishnan

Current Directions in Biomedical Engineering, Vol 9, Iss 1, Pp 721-

2023 Volume 724

Abstract: This study attempts to detect and differentiate Multi Drug Resistant (MDR) - Tuberculosis (TB) and Drug Sensitive (DS)-TB Chest Radiographs (CXR) using local texture descriptors and Ensemble Learning method. Studies report that CXR images contain ... ...

Abstract	This study attempts to detect and differentiate Multi Drug Resistant (MDR) - Tuberculosis (TB) and Drug Sensitive (DS)-TB Chest Radiographs (CXR) using local texture descriptors and Ensemble Learning method. Studies report that CXR images contain likelihood information of the drug resistance which can be utilized computationally. Initially, CXR images are subjected to lung fields segmentation using Reaction Diffusion Level Set method. Further, Local Directional Texture Pattern (LDTP) features are extracted from the segmented lungs to characterize the localized textural variations. Extreme Gradient Boosting (XGBoost) classifier is employed to differentiate DS-TB and MDR-TB images. The obtained results demonstrate the ability of extracted LDTP features to characterize nonspecific textural inhomogeneities in images by operating on its principal directions. XGBoost algorithm provides maximum accuracy of 93% and true positive rate of 94.6% in detecting MDR-TB. As the proposed study differentiates the MDR-TB condition using CXR images, its computerized diagnostics could be used in the early screening and followup of TB ridden patients for public health infection control in any setting.
Keywords	tuberculosis ; chest radiograph ; xgboost ; drug sensitive ; drug resistive ; Medicine ; R
Subject code	006
Language	English
Publishing date	2023-09-01T00:00:00Z
Publisher	De Gruyter
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Performance of SURF and SIFT Keypoints for the Automated Differentiation of Abnormality in Chest Radiographs.

Govindarajan, Satyavratan / Swaminathan, Ramakrishnan

Studies in health technology and informatics

2021 Volume 281, Page(s) 510–511

Abstract: In this work, automated abnormality detection using keypoint information from Speeded-Up Robust feature (SURF) and Scale Invariant Feature Transform (SIFT) descriptors in chest Radiographic (CR) images is investigated and compared. Computerized image ... ...

Abstract	In this work, automated abnormality detection using keypoint information from Speeded-Up Robust feature (SURF) and Scale Invariant Feature Transform (SIFT) descriptors in chest Radiographic (CR) images is investigated and compared. Computerized image analysis using artificial intelligence is crucial to detect subtle and non-specific alterations of Tuberculosis (TB). For this, the healthy and TB CRs are subjected to lung field segmentation. SURF and SIFT keypoints are extracted from the segmented lung images. Statistical features from keypoints, its scale and orientation are computed. Discrimination of TB from healthy is performed using SVM. Results show that the SURF and SIFT methods are able to extract local keypoint information in CRs. Linear SVM is found to perform better with precision of 88.9% and AUC of 91% in TB detection for combined features. Hence, the application of keypoint techniques is found to have clinical relevance in the automated screening of non-specific TB abnormalities using CRs.
MeSH term(s)	Algorithms ; Artificial Intelligence ; Image Processing, Computer-Assisted ; Pattern Recognition, Automated ; Radiography
Language	English
Publishing date	2021-05-27
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI210219
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Extreme Learning Machine based Differentiation of Pulmonary Tuberculosis in Chest Radiographs using Integrated Local Feature Descriptors.

Govindarajan, Satyavratan / Swaminathan, Ramakrishnan

Computer methods and programs in biomedicine

2021 Volume 204, Page(s) 106058

Abstract: Background and objective: Computer aided diagnostics of Pulmonary Tuberculosis in chest radiographs relies on the differentiation of subtle and non-specific alterations in the images. In this study, an attempt has been made to identify and classify ... ...

Abstract	Background and objective: Computer aided diagnostics of Pulmonary Tuberculosis in chest radiographs relies on the differentiation of subtle and non-specific alterations in the images. In this study, an attempt has been made to identify and classify Tuberculosis conditions from healthy subjects in chest radiographs using integrated local feature descriptors and variants of extreme learning machine. Methods: Lung fields in the chest images are segmented using Reaction Diffusion Level Set method. Local feature descriptors such as Median Robust Extended Local Binary Patterns and Gradient Local Ternary Patterns are extracted. Extreme Learning Machine (ELM) and Online Sequential ELM (OSELM) classifiers are employed to identify Tuberculosis conditions and, their performances are analysed using standard metrics. Results: Results show that the adopted segmentation method is able to delineate lung fields in both healthy and Tuberculosis images. Extracted features are statistically significant even in images with inter and intra subject variability. Sigmoid activation function yields accuracy and sensitivity values greater than 98% for both the classifiers. Highest sensitivity is observed with OSELM for minimal significant features in detecting Tuberculosis images. Conclusion: As ELM based method is able to differentiate the subtle changes in inter and intra subject variations of chest X-ray images, the proposed methodology seems to be useful for computer-based detection of Pulmonary Tuberculosis.
MeSH term(s)	Algorithms ; Diagnosis, Computer-Assisted ; Humans ; Lung ; Tomography, X-Ray Computed ; Tuberculosis, Pulmonary/diagnostic imaging
Language	English
Publishing date	2021-03-21
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	632564-6
ISSN	1872-7565 ; 0169-2607
ISSN (online)	1872-7565
ISSN	0169-2607
DOI	10.1016/j.cmpb.2021.106058
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Article ; Online: Differentiation of COVID-19 conditions in planar chest radiographs using optimized convolutional neural networks.

Govindarajan, Satyavratan / Swaminathan, Ramakrishnan

Applied intelligence (Dordrecht, Netherlands)

2020 Volume 51, Issue 5, Page(s) 2764–2775

Abstract: 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 ... ...

Abstract	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.
Language	English
Publishing date	2020-11-06
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1479519-X
ISSN	1573-7497 ; 0924-669X
ISSN (online)	1573-7497
ISSN	0924-669X
DOI	10.1007/s10489-020-01941-8
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Article ; Online: Differentiation of COVID-19 Conditions using Mediastinum Shape in Chest X-ray Images

Kumar Tulo Sukanta / Govindarajan Satyavratan / Ramu Palaniappan / Swaminathan Ramakrishnan

Current Directions in Biomedical Engineering, Vol 8, Iss 2, Pp 325-

2022 Volume 328

Abstract: In this work, an attempt has been made to analyze the shape variations in mediastinum for differentiation of Coronavirus Disease-2019 (COVID-19) and normal conditions in chest X-ray images. For this, the images are obtained from a publicly available ... ...

Abstract	In this work, an attempt has been made to analyze the shape variations in mediastinum for differentiation of Coronavirus Disease-2019 (COVID-19) and normal conditions in chest X-ray images. For this, the images are obtained from a publicly available dataset. Segmentation of mediastinum from the raw images is performed using Reaction Diffusion Level Set (RDLS) method. Shape-based features are extracted from the delineated mediastinum masks and are statistically analyzed. Further, the features are fed to two classifiers, namely, multi-layer perceptron and support vector machine for differentiation of normal and COVID-19 images. From the results, it is observed that the employed RDLS method is able to delineate mediastinum from the raw chest Xray images. Eight shape features are observed to be statistically significant. The mean values of these features are found to be distinctly higher for COVID-19 images as compared to normal images. Area under the curve of greater than 76.9% is achieved for both the classifiers. It appears that mediastinum could be used as a region of interest for computerized detection and mass screening of the disease.
Keywords	mediastinum ; covid-19 ; chest x-rays ; reaction diffusion level set ; shape features ; Medicine ; R
Language	English
Publishing date	2022-09-01T00:00:00Z
Publisher	De Gruyter
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Analysis of Tuberculosis in Chest Radiographs for Computerized Diagnosis using Bag of Keypoint Features.

Govindarajan, Satyavratan / Swaminathan, Ramakrishnan

Journal of medical systems

2019 Volume 43, Issue 4, Page(s) 87

Abstract: Chest radiography is the most preferred non-invasive imaging technique for early diagnosis of Tuberculosis (TB). However, lack of radiological expertise in TB detection leads to indiscriminate chest radiograph (CXR) screening. A modest classification ... ...

Abstract	Chest radiography is the most preferred non-invasive imaging technique for early diagnosis of Tuberculosis (TB). However, lack of radiological expertise in TB detection leads to indiscriminate chest radiograph (CXR) screening. A modest classification approach based on the local image description to detect subtle characteristics of TB using CXRs is highly recommended. In this work, an attempt has been made to classify normal and TB CXR images using Bag of Features (BoF) approach with Speeded-Up Robust Feature (SURF) descriptor. The images are obtained from a public database. Lung fields segmentation is performed using Distance Regularized Level Set (DRLS) formulation. The results of segmentation are validated against the ground truth images using similarity, overlap and area correlation measures. BoF approach with SURF keypoint descriptors is implemented to categorize the images using Multilayer Perceptron (MLP) classifier. The obtained results demonstrate that the DRLS method is able to delineate lung fields from CXR images. The BoF with SURF keypoint descriptor is able to characterize local attributes of normal and TB images. The segmentation results are found to be in high correlation with ground truth. MLP classifier is found to provide high Recall, Specificity (Spec), Accuracy, F-score and Area Under the Curve (AUC) values of 87.7%, 85.9%, 87.8%, 87.6% and 94% respectively between normal and abnormal images. The proposed computer aided diagnostic approach is found to perform better as compared to the existing methods. Thus, the study can be of significant assistance to physicians at the point of care in resource constrained regions.
MeSH term(s)	Algorithms ; Humans ; Radiographic Image Interpretation, Computer-Assisted/methods ; Radiography, Thoracic ; Sensitivity and Specificity ; Tuberculosis, Pulmonary/diagnosis ; Tuberculosis, Pulmonary/diagnostic imaging
Language	English
Publishing date	2019-02-28
Publishing country	United States
Document type	Journal Article
ZDB-ID	423488-1
ISSN	1573-689X ; 0148-5598
ISSN (online)	1573-689X
ISSN	0148-5598
DOI	10.1007/s10916-019-1222-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Classification of Normal and Cardiomegaly Conditions in Chest Radiographs Using Cardio-Mediastinal Features.

Govindarajan, Satyavratan / Tulo, Sukanta Kumar / Swaminathan, Ramakrishnan

Studies in health technology and informatics

2020 Volume 270, Page(s) 1225–1226

Abstract: In this study, an attempt has been made to differentiate normal and cardiomegaly using cardio-mediastinal ratiometric features and machine learning approaches. A total of 60 chest radiographs including normal and cardiomegaly subjects are considered from ...

Abstract	In this study, an attempt has been made to differentiate normal and cardiomegaly using cardio-mediastinal ratiometric features and machine learning approaches. A total of 60 chest radiographs including normal and cardiomegaly subjects are considered from a public dataset. The images are preprocessed using edge aware contrast enhancement technique to improve the edge contrast of lung boundaries. The mediastinal, cardiac and thoracic widths and their ratiometric indices are computed to characterize the morphological variations. The features are fed to three different classifiers for the differentiation of normal and cardiomegaly. Results show that the Linear discriminant analysis classifier is found to perform better with average values of recall 88.7%, precision 88.8%, and area under the curve 91.9%. Hence, the proposed computer aided diagnostic approach appears to be clinically significant to distinguish normal and cardiomegaly especially in remote and resource - poor settings.
MeSH term(s)	Cardiomegaly ; Heart ; Humans ; Lung ; Machine Learning ; Radiography, Thoracic
Language	English
Publishing date	2020-06-12
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI200374
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Differentiation of urothelial carcinoma in histopathology images using deep learning and visualization.

Mundhada, Aniruddha / Sundaram, Sandhya / Swaminathan, Ramakrishnan / D' Cruze, Lawrence / Govindarajan, Satyavratan / Makaram, Navaneethakrishna

Journal of pathology informatics

2022 Volume 14, Page(s) 100155

Abstract: Artificial Intelligence is a tool poised to transform healthcare, with use in diagnostics and therapeutics. The widespread use of digital pathology has been due to the advent of whole slide imaging. Cheaper storage for digital images, along with ... ...

Abstract	Artificial Intelligence is a tool poised to transform healthcare, with use in diagnostics and therapeutics. The widespread use of digital pathology has been due to the advent of whole slide imaging. Cheaper storage for digital images, along with unprecedented progress in artificial intelligence, have paved the synergy of these two fields. This has pushed the limits of traditional diagnosis using light microscopy, from a more subjective to a more objective method of looking at cases, incorporating grading too. The grading of histopathological images of urothelial carcinoma of the urinary bladder is important with direct implications for surgical management and prognosis. In this study, the aim is to classify urothelial carcinoma into low and high grade based on the WHO 2016 classification. The hematoxylin and eosin-stained transurethral resection of bladder tumor (TURBT) samples of both low and high grade non-invasive papillary urothelial carcinoma were digitally scanned. Patches were extracted from these whole slide images to feed into a deep learning (Convolution Neural Network: CNN) model. Patches were segregated if they had tumor tissue and only included for model training if a threshold of 90% of tumor tissue per patch was seen. Various parameters of the deep learning model, known as hyperparameters, were optimized to get the best accuracy for grading or classification into low- and high-grade urothelial carcinoma. The model was robust with an overall accuracy of 90% after hyperparameter tuning. Visualization in the form of a class activation map using Grad-CAM was done. This indicates that such a model can be used as a companion diagnostic tool for grading of urothelial carcinoma. The probable causes of this accuracy are summarized along with the limitations of this study and future work possible.
Language	English
Publishing date	2022-11-08
Publishing country	United States
Document type	Journal Article
ZDB-ID	2579241-6
ISSN	2153-3539 ; 2229-5089
ISSN (online)	2153-3539
ISSN	2229-5089
DOI	10.1016/j.jpi.2022.100155
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Differentiation of COVID-19 conditions in planar chest radiographs using optimized convolutional neural networks

Govindarajan, Satyavratan / Swaminathan, Ramakrishnan

Appl Intell

Abstract	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.
Keywords	covid19
Publisher	PMC
Document type	Article ; Online
DOI	10.1007/s10489-020-01941-8
Database	COVID19

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