Automatic X-ray COVID-19 Lung Image Classification System based on Multi-Level Thresholding and Support Vector Machine

Mahdy, Lamia Nabil; Ezzat, Kadry Ali; Elmousalami, Haytham H.; Ella, Hassan Aboul; Hassanien, Aboul Ella

doi:10.1101/2020.03.30.20047787

Cited by 90 publications

(60 citation statements)

References 25 publications

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“…In [30], authors have given a study about the total number of patients infected form Covid-19 and death cases all over the world. In [31], the authors recommended a deep based methodology (with vector gadget classifier) for the detection of patients infected from Covid-19 by using X-ray images. This method is beneficial to hospital doctors for early detecting the cases of covid-19 infected patients.…”

Section: Related Workmentioning

confidence: 99%

Deep learning based detection and analysis of COVID-19 on chest X-ray images

et al. 2020

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Covid-19 is a rapidly spreading viral disease that infects not only humans, but animals are also infected because of this disease. The daily life of human beings, their health, and the economy of a country are affected due to this deadly viral disease. Covid-19 is a common spreading disease, and till now, not a single country can prepare a vaccine for COVID-19. A clinical study of COVID-19 infected patients has shown that these types of patients are mostly infected from a lung infection after coming in contact with this disease. Chest x-ray (i.e., radiography) and chest CT are a more effective imaging technique for diagnosing lunge related problems. Still, a substantial chest x-ray is a lower cost process in comparison to chest CT. Deep learning is the most successful technique of machine learning, which provides useful analysis to study a large amount of chest x-ray images that can critically impact on screening of Covid-19. In this work, we have taken the PA view of chest x-ray scans for covid-19 affected patients as well as healthy patients. After cleaning up the images and applying data augmentation, we have used deep learningbased CNN models and compared their performance. We have compared Inception V3, Xception, and ResNeXt models and examined their accuracy. To analyze the model performance, 6432 chest x-ray scans samples have been collected from the Kaggle repository, out of which 5467 were used for training and 965 for validation. In result analysis, the Xception model gives the highest accuracy (i.e., 97.97%) for detecting Chest X-rays images as compared to other models. This work only focuses on possible methods of classifying covid-19 infected patients and does not claim any medical accuracy.

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Section: Related Workmentioning

confidence: 99%

Deep learning based detection and analysis of COVID-19 on chest X-ray images

et al. 2020

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“…This involved constructing an extensive dataset of CXR and CT images from various sources and developed a necessary yet compelling COVID-19 identification procedure utilizing DL and TL techniques. Mahdy et al [13] introduced a strategy for identifying COVID-19-infected persons utilizing CXR images. Multilevel thresholding and a support vector machine (SVM) were introduced to achieve high accuracy with images of the infected lungs of COVID-19 patients.…”

Section: Related Workmentioning

confidence: 99%

OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19

et al. 2020

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The quick spread of coronavirus disease (COVID-19) has become a global concern and affected more than 15 million confirmed patients as of July 2020. To combat this spread, clinical imaging, for example, X-ray images, can be utilized for diagnosis. Automatic identification software tools are essential to facilitate the screening of COVID-19 using X-ray images. This paper aims to classify COVID-19, normal, and pneumonia patients from chest X-ray images. As such, an Optimized Convolutional Neural network (OptCoNet) is proposed in this work for the automatic diagnosis of COVID-19. The proposed OptCoNet architecture is composed of optimized feature extraction and classification components. The Grey Wolf Optimizer (GWO) algorithm is used to optimize the hyperparameters for training the CNN layers. The proposed model is tested and compared with different classification strategies utilizing an openly accessible dataset of COVID-19, normal, and pneumonia images. The presented optimized CNN model provides accuracy, sensitivity, specificity, precision, and F1 score values of 97.78%, 97.75%, 96.25%, 92.88%, and 95.25%, respectively, which are better than those of state-of-the-art models. This proposed CNN model can help in the automatic screening of COVID-19 patients and decrease the burden on medicinal services frameworks.

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“…In [18,35] mammography images are classified using SVMs for benign and malignant masses, but in [18] ROIs are passed as input to the model. In [2,16,43] chest X-Ray images are used to predict tuberculosis and covid-19 using SVM classifier but do not focus on interpretability.…”

Section: Related Workmentioning

confidence: 99%

Interpreting SVM for medical images using Quadtree

Shukla

Verma

Abhishek

et al. 2020

Multimed Tools Appl

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In this paper, we propose a quadtree based approach to capture the spatial information of medical images for explaining nonlinear SVM prediction. In medical image classification, interpretability becomes important to understand why the adopted model works. Explaining an SVM prediction is difficult due to implicit mapping done in kernel classification is uninformative about the position of data points in the feature space and the nature of the separating hyperplane in the original space. The proposed method finds ROIs which contain the discriminative regions behind the prediction. Localization of the discriminative region in small boxes can help in interpreting the prediction by SVM. Quadtree decomposition is applied recursively before applying SVMs on sub images and model identified ROIs are highlighted. Pictorial results of experiments on various medical image datasets prove the effectiveness of this approach. We validate the correctness of our method by applying occlusion methods.

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Automatic X-ray COVID-19 Lung Image Classification System based on Multi-Level Thresholding and Support Vector Machine

Cited by 90 publications

References 25 publications

Deep learning based detection and analysis of COVID-19 on chest X-ray images

Deep learning based detection and analysis of COVID-19 on chest X-ray images

OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19

Interpreting SVM for medical images using Quadtree

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