Tapan Kumar Das¹, Pradeep Kumar Roy², Mohy Uddin³, Kathiravan Srinivasan¹, Chuan-Yu Chang^4,*, Shabbir Syed-Abdul⁵

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2413-2429, 2021, DOI:10.32604/cmc.2021.016698

Abstract Machine learning based image analysis for predicting and diagnosing certain diseases has been entirely trustworthy and even as efficient as a domain expert’s inspection. However, the style of non-transparency functioning by a trained machine learning system poses a more significant impediment for seamless knowledge trajectory, clinical mapping, and delusion tracing. In this proposed study, a deep learning based framework that employs deep convolution neural network (Deep-CNN), by utilizing both clinical presentations and conventional magnetic resonance imaging (MRI) investigations, for diagnosing tumors is explored. This research aims to develop a model that can be used for abnormality detection over MRI data… More >

R. Coriat

Oncologie, Vol.21, No.2, pp. 75-81, 2019, DOI:10.3166/onco-2019-0046

Abstract Pancreatic neuroendocrine tumours are the tumours developed at the expense of pancreas and require a specific diagnostic assessment. The imaging assessment of a pancreatic neuroendocrine tumour is useful for diagnosis as well as for surgical/medical treatment. Recently, a number of advances have been made in the field of imaging pancreatic neuroendocrine tumours, in particular in functional imaging using radiolabelled somatostatin analogues. In this review, we approach diagnostic progress by focusing on the advances of recent years. Thus, the interest of conventional imaging (scanner, abdominal ultrasound, and magnetic resonance imaging), ultrasound endoscopy and the place of functional imaging mainly with radiolabelled… More >

Gurparsad Singh Suri¹, Gurleen Kaur¹, Sara Moein^2,*

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 723-735, 2021, DOI:10.32604/iasc.2021.015049

Abstract Schizophrenia (SZ) is a mental heterogeneous psychiatric disorder with unknown cause. Neuroscientists postulate that it is related to brain networks. Recently, scientists applied machine learning (ML) and artificial intelligence for the detection, monitoring, and prognosis of a range of diseases, including SZ, because these techniques show a high performance in discovering an association between disease symptoms and disease. Regions of the brain have significant connections to the symptoms of SZ. ML has the power to detect these associations. ML interests researchers because of its ability to reduce the number of input features when the data are high dimensional. In this… More >

CHEN CAI¹, HAO SUN², LIANG HU³, ZHICHAO FAN^1,*

BIOCELL, Vol.45, No.2, pp. 229-257, 2021, DOI:10.32604/biocell.2021.014338

Abstract Integrin molecules are transmembrane αβ heterodimers involved in cell adhesion, trafficking, and signaling. Upon activation, integrins undergo dynamic conformational changes that regulate their affinity to ligands. The physiological functions and activation mechanisms of integrins have been heavily discussed in previous studies and reviews, but the fluorescence imaging techniques –which are powerful tools for biological studies– have not. Here we review the fluorescence labeling methods, imaging techniques, as well as Förster resonance energy transfer assays used to study integrin expression, localization, activation, and functions. More >

Lihan Cui¹, Lijie Yan¹, Xiaohu Zhao¹, Lin Yuan², Jing Jin³, Jingcheng Zhang^1,*

Phyton-International Journal of Experimental Botany, Vol.90, No.2, pp. 621-634, 2021, DOI:10.32604/phyton.2021.015511

Abstract Tea plant stresses threaten the quality of tea seriously. The technology corresponding to the fast detection and differentiation of stresses is of great significance for plant protection in tea plantation. In recent years, hyperspectral imaging technology has shown great potential in detecting and differentiating plant diseases, pests and some other stresses at the leaf level. However, the lack of studies at canopy level hampers the detection of tea plant stresses at a larger scale. In this study, based on the canopy-level hyperspectral imaging data, the methods for identifying and differentiating the three commonly occurred tea stresses (i.e., the tea leafhopper,… More >

Mustafa M. M. Al Qudah, Ahmad S. A. Mohamed^*, Syaheerah L. Lutfi

Computer Systems Science and Engineering, Vol.37, No.1, pp. 47-62, 2021, DOI:10.32604/csse.2021.015222

Abstract The thermal-based imaging technique has recently attracted the attention of researchers who are interested in the recognition of human affects due to its ability to measure the facial transient temperature, which is correlated with human affects and robustness against illumination changes. Therefore, studies have increasingly used the thermal imaging as a potential and supplemental solution to overcome the challenges of visual (RGB) imaging, such as the variation of light conditions and revealing original human affect. Moreover, the thermal-based imaging has shown promising results in the detection of psychophysiological signals, such as pulse rate and respiration rate in a contactless and… More >

Kathrine Rydén Suther^1,*, Charlotte de Lange^1,2, Henrik Brun³, Rolf Svendsmark¹, Bac Nguyen¹, Stig Larsen⁴, Bjarne Smevik¹, Arnt Eltvedt Fiane^5,6, Harald Lauritz Lindberg⁶, Einar Hopp¹

Congenital Heart Disease, Vol.16, No.2, pp. 107-121, 2021, DOI:10.32604/CHD.2021.014164

Abstract Background: Patency of the coronary arteries is an issue after reports of sudden cardiac death in patients with transposition of the great arteries (TGA) operated with arterial switch (ASO). Recent studies give rise to concern regarding the use of ionising radiation in congenital heart disease, and assessment of the coronary arteries with coronary MR angiography (CMRA) might be an attractive non-invasive, non-ionising imaging alternative in these patients. Theoretically, the use of 3.0T CMRA should improve the visualisation of the coronary arteries. The objective of this study was to assess feasibility of 3.0T CMRA at the coronary artery origins by comparing… More >

Sally M. Elghamrawy¹, Aboul Ella Hassnien^2,*, Vaclav Snasel³

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2353-2371, 2021, DOI:10.32604/cmc.2021.014767

Abstract Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic’s additional spread and early provide the appropriate treatment to the affected patients. This study aimed to develop a COVID-19 diagnosis and prediction (AIMDP) model that could identify patients with COVID-19 and distinguish it from other viral pneumonia signs detected in chest computed tomography (CT) scans. The proposed system uses convolutional neural networks (CNNs) as a deep learning technology to process hundreds of CT chest scan images and speeds up COVID-19 case prediction to facilitate its containment. We employed the whale optimization… More >

A. S. Al-Waisy¹, Mazin Abed Mohammed¹, Shumoos Al-Fahdawi¹, M. S. Maashi², Begonya Garcia-Zapirain³, Karrar Hameed Abdulkareem⁴, S. A. Mostafa⁵, Nallapaneni Manoj Kumar⁶, Dac-Nhuong Le^7,8,*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2409-2429, 2021, DOI:10.32604/cmc.2021.012955

Abstract Coronavirus (COVID-19) epidemic outbreak has devastating effects on daily lives and healthcare systems worldwide. This newly recognized virus is highly transmissible, and no clinically approved vaccine or antiviral medicine is currently available. Early diagnosis of infected patients through effective screening is needed to control the rapid spread of this virus. Chest radiography imaging is an effective diagnosis tool for COVID-19 virus and follow-up. Here, a novel hybrid multimodal deep learning system for identifying COVID-19 virus in chest X-ray (CX-R) images is developed and termed as the COVID-DeepNet system to aid expert radiologists in rapid and accurate image interpretation. First, Contrast-Limited… More >

Shuai Feng¹, Liu Jin¹, Yadan Wang¹, Wei Zhao^1,2, Hu Peng^1,*, Heyuan Qiao¹

CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.2, pp. 739-754, 2021, DOI:10.32604/cmes.2021.014027

Abstract The high frame rate (HFR) imaging technique requires only one emission event for imaging. Therefore, it can achieve ultrafast imaging with frame rates up to the kHz regime, which satisfies the frame rate requirements for imaging moving tissues in scientific research and clinics. Lu’s Fourier migration method is based on a non-diffraction beam to obtain HFR images and can improve computational speed and efficiency. However, in order to obtain high-quality images, Fourier migration needs to make full use of the spectrum of echo signals for imaging, which requires a large number of Fast Fourier Transform (FFT) points and increases the… More >