Xiaorui Zhang^1,2,3,*, Jie Zhou², Wei Sun^3,4, Sunil Kumar Jha⁵

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1123-1137, 2022, DOI:10.32604/cmc.2022.024589

Abstract The key to preventing the COVID-19 is to diagnose patients quickly and accurately. Studies have shown that using Convolutional Neural Networks (CNN) to analyze chest Computed Tomography (CT) images is helpful for timely COVID-19 diagnosis. However, personal privacy issues, public chest CT data sets are relatively few, which has limited CNN's application to COVID-19 diagnosis. Also, many CNNs have complex structures and massive parameters. Even if equipped with the dedicated Graphics Processing Unit (GPU) for acceleration, it still takes a long time, which is not conductive to widespread application. To solve above problems, this paper proposes a lightweight CNN classification… More >

Yanyue Li¹, Changlong Liu¹, Wenbo Bao^1,*, Baoqing Xue¹, Peng Lv¹, Nan Wang¹, Hui Li¹, Wenguo Ma²

FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.3, pp. 737-749, 2022, DOI:10.32604/fdmp.2022.019139

Abstract

Due to long-term water injection, often oilfields enter the so-called medium and high water cut stage, and it is difficult to achieve good oil recovery and water reduction through standard methods (single profile control and flooding measures). Therefore, in this study, a novel method based on “plugging, profile control, and flooding” being implemented at the same time is proposed. To assess the performances of this approach, physical simulations, computer tomography, and nuclear magnetic resonance are used. The results show that the combination of a gel plugging agent, a polymer microsphere flooding agent, and a high-efficiency oil displacement agent leads to… More >

Pengzhu Li, Martina Bačová, Robert Dalla-Pozza, Nikolaus Alexander Haas, Felix Sebastian Oberhoffer^*

Congenital Heart Disease, Vol.17, No.2, pp. 129-141, 2022, DOI:10.32604/CHD.2022.018300

Abstract Turner syndrome (TS) is a rare disorder affecting 25–50 in 100000 female newborns. Bicuspid aortic valve (BAV) is assumed to be the most common congenital heart defect (CHD) in TS. In literature, reported BAV prevalence in TS ranges between 14% and 34%. The specific BAV prevalence in TS is still unknown. The aim of this study was to give a more precise estimation of BAV prevalence in TS by conducting a meta-analysis of TS-studies, which detected BAV by either cardiac magnetic resonance imaging (MRI) or cardiac computed tomography (CT). We searched PubMed, Cochrane Library, and Web of Science databases to… More >

Ningbo Jing¹, Mingqiao Li¹, Lang Liu^2,*, Yutong Shen¹, Peijiao Yang¹, Xuebin Qin¹

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 465-476, 2022, DOI:10.32604/cmes.2022.018965

Abstract During mine filling, the caking in the pipeline and the waste rock in the filling slurry may cause serious safety accidents such as pipe blocking or explosion. Therefore, the visualization of the inner mine filling of the solid–liquid two-phase flow in the pipeline is important. This paper proposes a method based on capacitance tomography for the visualization of the solid–liquid distribution on the section of a filling pipe. A feedback network is used for electrical capacitance tomography reconstruction. This reconstruction method uses radial basis function neural network fitting to determine the relationship between the capacitance vector and medium distribution error.… More >

Yasmeen Al-Saeed^1,2, Wael A. Gab-Allah¹, Hassan Soliman¹, Maysoon F. Abulkhair³, Wafaa M. Shalash⁴, Mohammed Elmogy^1,*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4871-4894, 2022, DOI:10.32604/cmc.2022.023638

Abstract One of the leading causes of mortality worldwide is liver cancer. The earlier the detection of hepatic tumors, the lower the mortality rate. This paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign tumors. Segmenting hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range, intensity values overlap between the liver and neighboring organs, high noise from computed tomography scanner, and large variance in tumors shapes. The proposed method consists of three main stages; liver segmentation using Fast… More >

Batyrkhan Omarov^1,2,3, Azhar Tursynova^1,*, Octavian Postolache⁴, Khaled Gamry⁵, Aidar Batyrbekov⁵, Sapargali Aldeshov^6,7, Zhanar Azhibekova⁹, Marat Nurtas^5,8, Akbayan Aliyeva⁶, Kadrzhan Shiyapov^10,11

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4701-4717, 2022, DOI:10.32604/cmc.2022.020998

Abstract The task of segmentation of brain regions affected by ischemic stroke is help to tackle important challenges of modern stroke imaging analysis. Unfortunately, at the moment, the models for solving this problem using machine learning methods are far from ideal. In this paper, we consider a modified 3D UNet architecture to improve the quality of stroke segmentation based on 3D computed tomography images. We use the ISLES 2018 (Ischemic Stroke Lesion Segmentation Challenge 2018) open dataset to train and test the proposed model. Interpretation of the obtained results, as well as the ideas for further experiments are included in the… More >

Xuebin Qin^1,*, Yutong Shen¹, Jiachen Hu¹, Mingqiao Li¹, Peijiao Yang¹, Chenchen Ji¹, Xinlong Zhu²

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1699-1717, 2022, DOI:10.32604/cmes.2022.018234

Abstract Electrical capacitance tomography (ECT) has great application potential in multiphase process monitoring, and its visualization results are of great significance for studying the changes in two-phase flow in closed environments. In this paper, compressed sensing (CS) theory based on dictionary learning is introduced to the inverse problem of ECT, and the K-SVD algorithm is used to learn the overcomplete dictionary to establish a nonlinear mapping between observed capacitance and sparse space. Because the trained overcomplete dictionary has the property to match few features of interest in the reconstructed image of ECT, it is not necessary to rely on the sparsity… More >

Muhammad Ibrahim Khalil¹, Samabia Tehsin¹, Mamoona Humayun², N.Z Jhanjhi^3,4,*, Mohammed A. AlZain⁵

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3251-3265, 2022, DOI:10.32604/cmc.2022.020561

Abstract Medical Imaging Segmentation is an essential technique for modern medical applications. It is the foundation of many aspects of clinical diagnosis, oncology, and computer-integrated surgical intervention. Although significant successes have been achieved in the segmentation of medical images, DL (deep learning) approaches. Manual delineation of OARs (organs at risk) is vastly dominant but it is prone to errors given the complex irregularities in shape, low texture diversity between tissues and adjacent blood area, patient-wide location of organisms, and weak soft tissue contrast across adjacent organs in CT images. Till now several models have been implemented on multi organs segmentation but… More >

Indrajeet Kumar¹, Sultan S. Alshamrani², Abhishek Kumar³, Jyoti Rawat⁴, Kamred Udham Singh¹, Mamoon Rashid^5,*, Ahmed Saeed AlGhamdi⁶

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 451-468, 2022, DOI:10.32604/cmc.2022.019443

Abstract Early diagnosis of a pandemic disease like COVID-19 can help deal with a dire situation and help radiologists and other experts manage human resources more effectively. In a recent pandemic, laboratories perform diagnostics manually, which requires a lot of time and expertise of the laboratorial technicians to yield accurate results. Moreover, the cost of kits is high, and well-equipped labs are needed to perform this test. Therefore, other means of diagnosis is highly desirable. Radiography is one of the existing methods that finds its use in the diagnosis of COVID-19. The radiography observes change in Computed Tomography (CT) chest images… More >

Aihong Yu¹, Zhe Liu^1,*, Victor S. Sheng², Yuqing Song¹, Xuesheng Liu³, Chongya Ma⁴, Wenqiang Wang¹, Cong Ma¹

Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 589-599, 2021, DOI:10.32604/iasc.2021.019513

Abstract Liver cancer is one of frequent causes of death from malignancy in the world. Owing to the outstanding advantages of computer-aided diagnosis and deep learning, fully automatic segmentation of computed tomography (CT) images turned into a research hotspot over the years. The liver has quite low contrast with the surrounding tissues, together with its lesion areas are thoroughly complex. To deal with these problems, we proposed effective methods for enhancing features and processed public datasets from Liver Tumor Segmentation Challenge (LITS) for the verification. In this experiment, data pre-processing based on the image enhancement and noise reduction. This study redesigned… More >