Enhancement of soft-tissue contrast in cone-beam CT using an anti-scatter grid with a sparse sampling approach

Cho, Sanghoon; Lim, SM; Kim, Chang-Hwan; Wi, Sunhee; Kwon, Tae-Yong; Youn, Won Sik; Lee, Sang Hyun; Kang, Bo Sun; Cho, Seungryong

doi:10.1016/j.ejmp.2020.01.004

Cited by 13 publications

(8 citation statements)

References 24 publications

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“…The proposed PSO-Guided WOA algorithm' computational complexity will be discussed here according to Algorithm (4).Let n as number of population; M t as number of iterations. For each part of the algorithm, the time complexity can be defined as:…”

Section: ) Computational Complexity Analysismentioning

confidence: 99%

“…Such studies are centered around the screening process to discover early-stage patients, the proposed treatment protocol, and the assessment for various stages and recovery of treated patients. The image modalities including Chest X-ray and Computed Tomography (CT) are non-invasive and are widely used in hospitals to detect both the presence and severity of COVID-19 pneumonia [3] , [4] . Compared to CT, even though X-ray is more accessible in hospitals around the world, X-ray images can be considered less sensitive than CT scans for the investigation of COVID-19 patients.…”

Section: Introductionmentioning

confidence: 99%

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Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images

et al. 2020

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Diagnosis is a critical preventive step in Coronavirus research which has similar manifestations with other types of pneumonia. CT scans and X-rays play an important role in that direction. However, processing chest CT images and using them to accurately diagnose COVID-19 is a computationally expensive task. Machine Learning techniques have the potential to overcome this challenge. This paper proposes two optimization algorithms for feature selection and classification of COVID-19. The proposed framework has three cascaded phases. Firstly, the features are extracted from the CT scans using a Convolutional Neural Network (CNN) named AlexNet. Secondly, a proposed features selection algorithm, Guided Whale Optimization Algorithm (Guided WOA) based on Stochastic Fractal Search (SFS), is then applied followed by balancing the selected features. Finally, a proposed voting classifier, Guided WOA based on Particle Swarm Optimization (PSO), aggregates different classifiers' predictions to choose the most voted class. This increases the chance that individual classifiers, e.g. Support Vector Machine (SVM), Neural Networks (NN), k-Nearest Neighbor (KNN), and Decision Trees (DT), to show significant discrepancies. Two datasets are used to test the proposed model: CT images containing clinical findings of positive COVID-19 and CT images negative COVID-19. The proposed feature selection algorithm (SFS-Guided WOA) is compared with other optimization algorithms widely used in recent literature to validate its efficiency. The proposed voting classifier (PSO-Guided-WOA) achieved AUC (area under the curve) of 0.995 that is superior to other voting classifiers in terms of performance metrics. Wilcoxon rank-sum, ANOVA, and T-test statistical tests are applied to statistically assess the quality of the proposed algorithms as well.

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Section: ) Computational Complexity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images

et al. 2020

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“…Both CT and CXR can indicate abnormalities of lung disease, including COVID-19 [2] , [3] . Even though CXR is more accessible around the world hospitals, chest CT scan is more sensitive than CXR for early detection of COVID-19 disease changes, as well as for staging of the disease and monitoring progression [4] , [5] . CT images, on the other hand, are considered a powerful analysis tool [6] , [7] widely applied to biomedical imaging [8] and clinical diagnosis [9] and provide non-destructive 3D visualization of internal structures.…”

Section: Introductionmentioning

confidence: 99%

CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection

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et al. 2021

Applied Soft Computing

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“…Among these, computed tomography and CXR have become very popular and are often used to detect lung disease abnormalities, including COVID-19 [ 5 ]. CXR is more accessible in hospitals worldwide than computed tomography, but for staging COVID-19 disease, early detection of disease changes, and monitoring progress, a computed tomography image is more precise than a CXR image of the chest [ 6 , 7 ]. Moreover, computed tomography images are a robust tool broadly used in medical imaging [ 8 ] and diagnosis [ 9 ] applications.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization

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Durdu

et al. 2022

Computers in Biology and Medicine

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The coronavirus outbreak 2019, called COVID-19, which originated in Wuhan, negatively affected the lives of millions of people and many people died from this infection. To prevent the spread of the disease, which is still in effect, various restriction decisions have been taken all over the world. In addition, the number of COVID-19 tests has been increased to quarantine infected people. However, due to the problems encountered in the supply of RT-PCR tests and the ease of obtaining Computed Tomography and X-ray images, imaging-based methods have become very popular in the diagnosis of COVID-19. Therefore, studies using these images to classify COVID-19 have increased. This paper presents a classification method for computed tomography chest images in the COVID-19 Radiography Database using features extracted by popular Convolutional Neural Networks (CNN) models (AlexNet, ResNet18, ResNet50, Inceptionv3, Densenet201, Inceptionresnetv2, MobileNetv2, GoogleNet). The determination of hyperparameters of Machine Learning (ML) algorithms by Bayesian optimization, and ANN-based image segmentation are the two main contributions in this study. First of all, lung segmentation is performed automatically from the raw image with Artificial Neural Networks (ANNs). To ensure data diversity, data augmentation is applied to the COVID-19 classes, which are fewer than the other two classes. Then these images are applied as input to five different CNN models. The features extracted from each CNN model are given as input to four different ML algorithms, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Naive Bayes (NB), and Decision Tree (DT) for classification. To achieve the most successful classification accuracy, the hyperparameters of each ML algorithm are determined using Bayesian optimization. With the classification made using these hyperparameters, the highest success is obtained as 96.29% with the DenseNet201 model and SVM algorithm. The Sensitivity, Precision, Specificity, MCC, and F1-Score metric values for this structure are 0.9642, 0.9642, 0.9812, 0.9641 and 0.9453, respectively. These results showed that ML methods with the most optimum hyperparameters can produce successful results.

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Enhancement of soft-tissue contrast in cone-beam CT using an anti-scatter grid with a sparse sampling approach

Cited by 13 publications

References 24 publications

Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images

Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images

CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection

COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization

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