Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images

Bourouis, Sami; Alharbi, Abdullah; Bouguila, Nizar

doi:10.3390/jimaging7010007

Cited by 10 publications

(13 citation statements)

References 52 publications

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“…The proposed framework outperformed the first stage of the existing frameworks for COVID-19 detection. Figure 10 shows the first-stage performance of the proposed method for chest X-rays with [41][42][43][44][45][46][47], and Figure 11 shows the comparison of the first-stage performance of the proposed technique for CT scans with [21] and [48][49][50][51][52][53][54][55].…”

Section: Comparison With Existing Modelsmentioning

confidence: 99%

A Dual-Stage Vocabulary of Features (VoF)-Based Technique for COVID-19 Variants’ Classification

Jamil

Rahman

2021

Applied Sciences

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Novel coronavirus, known as COVID-19, is a very dangerous virus. Initially detected in China, it has since spread all over the world causing many deaths. There are several variants of COVID-19, which have been categorized into two major groups. These groups are variants of concern and variants of interest. Variants of concern are more dangerous, and there is a need to develop a system that can detect and classify COVID-19 and its variants without touching an infected person. In this paper, we propose a dual-stage-based deep learning framework to detect and classify COVID-19 and its variants. CT scans and chest X-ray images are used. Initially, the detection is done through a convolutional neural network, and then spatial features are extracted with deep convolutional models, while handcrafted features are extracted from several handcrafted descriptors. Both spatial and handcrafted features are combined to make a feature vector. This feature vector is called the vocabulary of features (VoF), as it contains spatial and handcrafted features. This feature vector is fed as an input to the classifier to classify different variants. The proposed model is evaluated based on accuracy, F1-score, specificity, sensitivity, specificity, Cohen’s kappa, and classification error. The experimental results show that the proposed method outperforms all the existing state-of-the-art methods.

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Section: Comparison With Existing Modelsmentioning

confidence: 99%

A Dual-Stage Vocabulary of Features (VoF)-Based Technique for COVID-19 Variants’ Classification

Jamil

Rahman

2021

Applied Sciences

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“…In this context, the work in this paper is based on recent research outcomes that have shown the importance of some specific distributions for complex visual vectors modeling based on Dirichlet and scaled Dirichlet mixture distributions [16,19]. In particular, some recent studies have shown that the derived model, the called shifted-scaled Dirichlet mixture model (SSDMM), can be applied successfully for a variety of applications [20,21]. SSDMM is presented as a powerful generalization of both Dirichlet and scaled Dirichlet where the term shifted, here, means a perturbation in the simplex.…”

Section: Introductionmentioning

confidence: 99%

Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition

Alharithi

Almulihi

Bourouis

et al. 2021

Sensors

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In this paper, we propose a novel hybrid discriminative learning approach based on shifted-scaled Dirichlet mixture model (SSDMM) and Support Vector Machines (SVMs) to address some challenging problems of medical data categorization and recognition. The main goal is to capture accurately the intrinsic nature of biomedical images by considering the desirable properties of both generative and discriminative models. To achieve this objective, we propose to derive new data-based SVM kernels generated from the developed mixture model SSDMM. The proposed approach includes the following steps: the extraction of robust local descriptors, the learning of the developed mixture model via the expectation–maximization (EM) algorithm, and finally the building of three SVM kernels for data categorization and classification. The potential of the implemented framework is illustrated through two challenging problems that concern the categorization of retinal images into normal or diabetic cases and the recognition of lung diseases in chest X-rays (CXR) images. The obtained results demonstrate the merits of our hybrid approach as compared to other methods.

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“…Authors in [ 52 ] used CNN to detect COVID-19 from a chest X-ray. Different techniques have been used for COVID-19 detection in [ 53 , 54 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of COVID-19 and Pneumonia Using Deep Transfer Learning

Mahin

Tonmoy

Islam

et al. 2021

Journal of Healthcare Engineering

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The World Health Organization (WHO) recognized COVID-19 as the cause of a global pandemic in 2019. COVID-19 is caused by SARS-CoV-2, which was identified in China in late December 2019 and is indeed referred to as the severe acute respiratory syndrome coronavirus-2. The whole globe was hit within several months. As millions of individuals around the world are infected with COVID-19, it has become a global health concern. The disease is usually contagious, and those who are infected can quickly pass it on to others with whom they come into contact. As a result, monitoring is an effective way to stop the virus from spreading further. Another disease caused by a virus similar to COVID-19 is pneumonia. The severity of pneumonia can range from minor to life-threatening. This is particularly hazardous for children, people over 65 years of age, and those with health problems or immune systems that are affected. In this paper, we have classified COVID-19 and pneumonia using deep transfer learning. Because there has been extensive research on this subject, the developed method concentrates on boosting precision and employs a transfer learning technique as well as a model that is custom-made. Different pretrained deep convolutional neural network (CNN) models were used to extract deep features. The classification accuracy was used to measure performance to a great extent. According to the findings of this study, deep transfer learning can detect COVID-19 and pneumonia from CXR images. Pretrained customized models such as MobileNetV2 had a 98% accuracy, InceptionV3 had a 96.92% accuracy, EffNet threshold had a 94.95% accuracy, and VGG19 had a 92.82% accuracy. MobileNetV2 has the best accuracy of all of these models.

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Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images

Cited by 10 publications

References 52 publications

A Dual-Stage Vocabulary of Features (VoF)-Based Technique for COVID-19 Variants’ Classification

A Dual-Stage Vocabulary of Features (VoF)-Based Technique for COVID-19 Variants’ Classification

Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition

Classification of COVID-19 and Pneumonia Using Deep Transfer Learning

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