Elin Xiang^1,2, Shumin Yang^1,*, Chunjie Cao³, Xinge Liu¹, Guanyun Peng⁴, Lili Shang¹, Genlin Tian¹, Qianli Ma¹, Jianfeng Ma¹

Journal of Renewable Materials, Vol.9, No.9, pp. 1531-1540, 2021, DOI:10.32604/jrm.2021.015346

Abstract In recent years, bamboo has been widely used in a broad range of applications, a thorough understanding of the structural characteristics of bamboo nodes is essential for better processing and manufacturing of biomimetic materials. This study investigated the complex anatomical structure for the nodes of two bamboo species, Indocalamus latifolius (Keng) McClure and Shibataea chinensis Nakai, using a high-resolution X-ray microtomography (μCT). The results show that the vascular bundle system in the nodal region of I. latifolius and S. chinensis is a net-like structure composed of horizontal and axial vascular bundles. Furthermore, the fiber sheath surrounding metaxylem vessels tended to… More >

Yichen Lu¹, Zongning Chen¹, Enyu Guo^1,*, Xiangqing Kong², Huijun Kang¹, Yanjin Xu³, Rengeng Li⁴, Guohua Fan⁴, Tongmin Wang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.2, pp. 705-720, 2021, DOI:10.32604/cmes.2021.015426

Abstract In this study, the real 3D model of the feather shaft that is composed of medulla and cortex is characterized by X-ray computer tomography, and the structural features are quantitatively analyzed. Compression and tensile tests are conducted to evaluate the mechanical performance of the feather shaft and cortex at different regions. The analysis of the 3D model shows that the medulla accounts for ∼70% of the shaft volume and exhibits a closed-cell foam-like structure, with a porosity of 59%. The cells in the medulla show dodecahedron and decahedron morphology and have an equivalent diameter of ∼30 μm. In axial compression,… More >

Javaria Amin¹, Muhammad Sharif², Muhammad Almas Anjum³, Yunyoung Nam^4,*, Seifedine Kadry⁵, David Taniar⁶

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2451-2467, 2021, DOI:10.32604/cmc.2021.014199

Abstract Coronavirus 19 (COVID-19) can cause severe pneumonia that may be fatal. Correct diagnosis is essential. Computed tomography (CT) usefully detects symptoms of COVID-19 infection. In this retrospective study, we present an improved framework for detection of COVID-19 infection on CT images; the steps include pre-processing, segmentation, feature extraction/fusion/selection, and classification. In the pre-processing phase, a Gabor wavelet filter is applied to enhance image intensities. A marker-based, watershed controlled approach with thresholding is used to isolate the lung region. In the segmentation phase, COVID-19 lesions are segmented using an encoder-/decoder-based deep learning model in which deepLabv3 serves as the bottleneck and… More >

Aqib Ali^1,2, Wali Khan Mashwani³, Samreen Naeem², Muhammad Irfan Uddin⁴, Wiyada Kumam⁵, Poom Kumam^6,7,*, Hussam Alrabaiah^8,9, Farrukh Jamal¹⁰, Christophe Chesneau¹¹

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 391-407, 2021, DOI:10.32604/cmc.2021.016037

Abstract The purpose of this research is the segmentation of lungs computed tomography (CT) scan for the diagnosis of COVID-19 by using machine learning methods. Our dataset contains data from patients who are prone to the epidemic. It contains three types of lungs CT images (Normal, Pneumonia, and COVID-19) collected from two different sources; the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur, Pakistan, and the second one is a publicly free available medical imaging database known as Radiopaedia. For the preprocessing, a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an… More >

Haikel Alhichri^*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3581-3599, 2021, DOI:10.32604/cmc.2021.015399

Abstract In January 2020, the World Health Organization declared a global health emergency concerning the spread of a new coronavirus disease, which was later named COVID-19. Early and fast diagnosis and isolation of COVID-19 patients have proven to be instrumental in limiting the spread of the disease. Computed tomography (CT) is a promising imaging method for fast diagnosis of COVID-19. In this study, we develop a unique preprocessing step to resize CT chest images to a fixed size (256 × 256 pixels) that preserves the aspect ratio and reduces image loss. Then, we present a deep learning (DL) method to classify… More >

Mohamad Mahmoud Al Rahhal¹, Yakoub Bazi^2,*, Rami M. Jomaa³, Mansour Zuair², Naif Al Ajlan²

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2093-2110, 2021, DOI:10.32604/cmc.2021.014956

Abstract With the rapid spread of the coronavirus disease 2019 (COVID-19) worldwide, the establishment of an accurate and fast process to diagnose the disease is important. The routine real-time reverse transcription-polymerase chain reaction (rRT-PCR) test that is currently used does not provide such high accuracy or speed in the screening process. Among the good choices for an accurate and fast test to screen COVID-19 are deep learning techniques. In this study, a new convolutional neural network (CNN) framework for COVID-19 detection using computed tomography (CT) images is proposed. The EfficientNet architecture is applied as the backbone structure of the proposed network,… More >

Prabakaran Rajamanickam¹, Shiloah Elizabeth Darmanayagam^1,*, Sunil Retmin Raj Cyril Raj²

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 709-722, 2021, DOI:10.32604/cmc.2021.014347

Abstract Liver Segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography (CT) images. The segmentation of hepatic organ is more intricate task, owing to the fact that it possesses a sizeable quantum of vascularization. This paper proposes an algorithm for automatic seed point selection using energy feature for use in level set algorithm for segmentation of liver region in CT scans. The effectiveness of the method can be determined when used in a model to classify the liver CT images as tumorous or not. This involves segmentation of the region of interest (ROI) from… More >

Wenru Fan, Chi Wang^*

Structural Durability & Health Monitoring, Vol.14, No.4, pp. 303-314, 2020, DOI:10.32604/sdhm.2020.011009

Abstract Due to the widespread use of carbon fiber reinforced polymer/plastic (CFRP), the nondestructive structural health monitoring for CFRP is playing an increasingly essential role. As a nonradiative, noninvasive and nondestructive detection technique, planar electrical capacitance tomography (PECT) electrodes array is employed in this paper to reconstruct the damage image according to the calculated dielectric constant changes. The shape and duty ratio of PECT electrodes are optimized according to the relations between sensitivity distribution and the dielectric constant of different anisotropic degrees. The sensitivity matrix of optimized PECT sensor is more uniform as the result shows, because the sensitivity of insensitivity… More >

Xiaoyong Wang^*

FDMP-Fluid Dynamics & Materials Processing, Vol.16, No.5, pp. 857-869, 2020, DOI:10.32604/fdmp.2020.09565

Abstract To explore the inherent characteristics of combustion-induced heat transfer in a flat flame furnace, a sophisticated hybrid method is introduced by combining a computer-based tomography (CT)-algebraic iterative algorithm and Tunable Diode Laser Absorption Spectroscopy (TDLAS). This technique is used to analyze the distribution of vapor concentration and furnace temperature. It is shown that by using this strategy a variety of details can be obtained, which would otherwise be out of reach. More >

Liying Wang¹ , Zhiqiang Xu², Shuihua Wang^3,4,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 1001-1015, 2020, DOI:10.32604/cmes.2020.010753

Abstract Artificial Intelligence (AI) becomes one hotspot in the field of the medical images analysis and provides rather promising solution. Although some research has been explored in smart diagnosis for the common diseases of urinary system, some problems remain unsolved completely A nine-layer Convolutional Neural Network (CNN) is proposed in this paper to classify the renal Computed Tomography (CT) images. Four group of comparative experiments prove the structure of this CNN is optimal and can achieve good performance with average accuracy about 92.07 ± 1.67%. Although our renal CT data is not very large, we do augment the training data by… More >