Distinguishing normal and pulmonary edema chest x-ray using Gabor filter and SVM

Kumar, Atul; Wang, Yen‐Yu; Liu, Kai-Che; Tsai, I-Chen; Huang, Ching-Chun; Hùng, Nguyễn Tuấn

doi:10.1109/isbb.2014.6820918

Cited by 14 publications

(10 citation statements)

References 11 publications

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“…This approach can help doctors detect the degree of infection easily and accurately. Kumar et al [103] used a machine learning algorithm to perform texture analysis of chest X-rays. They used a Gabor filter and SVM to distinguish normal chest and pulmonary edema in chest radiographs, and they obtained an AUC of 0.96.…”

Section: Specific Disease Detectionmentioning

confidence: 99%

Computer-aided detection in chest radiography based on artificial intelligence: a survey

et al. 2018

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As the most common examination tool in medical practice, chest radiography has important clinical value in the diagnosis of disease. Thus, the automatic detection of chest disease based on chest radiography has become one of the hot topics in medical imaging research. Based on the clinical applications, the study conducts a comprehensive survey on computer-aided detection (CAD) systems, and especially focuses on the artificial intelligence technology applied in chest radiography. The paper presents several common chest X-ray datasets and briefly introduces general image preprocessing procedures, such as contrast enhancement and segmentation, and bone suppression techniques that are applied to chest radiography. Then, the CAD system in the detection of specific disease (pulmonary nodules, tuberculosis, and interstitial lung diseases) and multiple diseases is described, focusing on the basic principles of the algorithm, the data used in the study, the evaluation measures, and the results. Finally, the paper summarizes the CAD system in chest radiography based on artificial intelligence and discusses the existing problems and trends.

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Section: Specific Disease Detectionmentioning

confidence: 99%

Computer-aided detection in chest radiography based on artificial intelligence: a survey

et al. 2018

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“…Authors then used wavelet transform for texture feature extraction. Kumar et al [10] proposed an approach which was based on machine learning algorithm. Authors used texture analysis of chest X-rays for pulmonary edema detection.…”

Section: Introductionmentioning

confidence: 99%

The investigation of multiresolution approaches for chest X-ray image based COVID-19 detection

Ismael

Şengür

2020

Health Inf Sci Syst

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COVID-19 is a novel virus, which has a fast spreading rate, and now it is seen all around the world. The case and death numbers are increasing day by day. Some tests have been used to determine the COVID-19. Chest X-ray and chest computerized tomography (CT) are two important imaging tools for determination and monitoring of COVID-19. And new methods have been searching for determination of the COVID-19. In this paper, the investigation of various multiresolution approaches in detection of COVID-19 is carried out. Chest X-ray images are used as input to the proposed approach. As recent trend in machine learning shifts toward the deep learning, we would like to show that the traditional methods such as multiresolution approaches are still effective. To this end, the well-known multiresolution approaches namely Wavelet, Shearlet and Contourlet transforms are used to decompose the chest X-ray images and the entropy and the normalized energy approaches are employed for feature extraction from the decomposed chest X-ray images. Entropy and energy features are generally accompanied with the multiresolution approaches in texture recognition applications. The extreme learning machines (ELM) classifier is considered in the classification stage of the proposed study. A dataset containing 361 different COVID-19 chest X-ray images and 200 normal (healthy) chest X-ray images are used in the experimental works. The performance evaluation is carried out by employing various metric namely accuracy, sensitivity, specificity and precision. As deep learning is mentioned, a comparison between proposed multiresolution approaches and deep learning approaches is also carried out. To this end, deep feature extraction and fine-tuning of pretrained convolutional neural networks (CNNs) are considered. For deep feature extraction, pretrained, ResNet50 model is employed. For classification of the deep features, the Support Vector Machines (SVM) classifier is used. The ResNet50 model is also used in the fine-tuning. The experimental works show that multiresolution approaches produced better performance than the deep learning approaches. Especially, Shearlet transform outperformed at all. 99.29% accuracy score is obtained by using Shearlet transform.

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“…It is proved experimentally that augmenting the original imbalanced dataset with a proposed method improves performance of chest pathology classification. It is explored that CNN based abnormality detection in frontal CXRs has found in the existing literature to be insufficient for making comparison of various detection techniques either due to studies reported on private datasets are not reporting the test scores in proper detail [9]. In order to overcome these difficulties, we have used the publicly available three CXRs datasets and studied the performance of various CNN architectures on different abnormalities.…”

Section: Validate the Model For The Optimal Resultsmentioning

confidence: 99%

Image pre-processing: enhance the performance of medical image classification using various data augmentation technique

Rama¹,

Nalini²,

Kumaravel³

2019

TIPCV

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The medical image classification system is an important subject in the field of biotechnology. Here, the network is trained with a large amount of computation to obtain high accuracy rate [1]. Chest X-rays images (CXRs) are broadly used in identifying the abnormalities in the chest area. Automatic detecting techniques are used in most of the diagnosing process, to improve the accuracy rate of abnormality detection. The main objective of this work is to prove the range of error, loss and accuracy by using the convolution neural networks (CNNs) model for detecting tuberculosis in the chest images. Tremendous progress has been made in deep learning models for classifying medical images.

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Distinguishing normal and pulmonary edema chest x-ray using Gabor filter and SVM

Cited by 14 publications

References 11 publications

Computer-aided detection in chest radiography based on artificial intelligence: a survey

Computer-aided detection in chest radiography based on artificial intelligence: a survey

The investigation of multiresolution approaches for chest X-ray image based COVID-19 detection

Image pre-processing: enhance the performance of medical image classification using various data augmentation technique

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