Computer-aided detection of interstitial lung diseases: A texture approach

Plankis, Tomas; Juozapavičius, Algimantas; Stašienė, Eglė; Usonis, Vytautas

doi:10.15388/na.2017.3.8

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…[93]. Interstitial lung disease (ILD) is a group of basic pathological lesions with diffuse pulmonary parenchyma, alveolar inflammation, and interstitial fibrosis, including interstitial pulmonary edema, allergic pneumonia, idiopathic pulmonary interstitial fibrosis, sarcoidosis, and lung lymphatic cancer [94, 95]. Cases with different interstitial lesions behave very similarly on light sheets, even for professionals, it is difficult to distinguish between normal and non-normal tissue based on texture.…”

Section: Specific Disease Detectionmentioning

confidence: 99%

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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%

“…Plankis et al [94] developed a flexible scheme for CAD of ILD. This approach can detect a variety of pathological features of interstitial lung tissue based on an active contour algorithm which can select the lung region.…”

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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“…SVM classifier was used to in classification stage of the proposed work. Plankis et al [9] proposed a computer aided design for interstitial lung disease detection. The proposed approach was based on active contour method, which determined the region of interests on chest X-ray images.…”

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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