J. Anitha^*, M. Kalaiarasu

Computer Systems Science and Engineering, Vol.43, No.1, pp. 363-379, 2022, DOI:10.32604/csse.2022.022402 - 23 March 2022

Abstract Digital Image Processing (DIP) is a well-developed field in the biological sciences which involves classification and detection of tumour. In medical science, automatic brain tumor diagnosis is an important phase. Brain tumor detection is performed by Computer-Aided Diagnosis (CAD) systems. The human image creation is greatly achieved by an approach namely medical imaging which is exploited for medical and research purposes. Recently Automatic brain tumor detection from MRI images has become the emerging research area of medical research. Brain tumor diagnosis mainly performed for obtaining exact location, orientation and area of abnormal tissues. Cancer and… More >

Duraimurugan Nagendiran^1,*, S. P. Chokkalingam²

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 983-996, 2022, DOI:10.32604/iasc.2022.023982 - 08 February 2022

Abstract Uncontrollable growth of cells may lead to brain tumors and may cause permanent damages to the brain or even death. To make early diagnosis and treatment, identifying the position and size of tumors is identified as a tedious and troublesome problem among the existing computer-aided diagnosis systems. Moreover, the progression of tumors may vary among the patients with respect to shape, location, and volume. Therefore, to effectively classify and diagnose the brain tumor images according to severity stages follows the sequence of processing such as pre-processing, segmentation, feature extraction, and classification techniques to carrying out 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 - 26 January 2022

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… More >

Jing Lu¹, Yan Wu^2,#, Mingyan Hu¹, Yao Xiong¹, Yapeng Zhou¹, Ziliang Zhao¹, Liutong Shang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 365-377, 2022, DOI:10.32604/cmes.2021.017897 - 29 November 2021

Abstract Background: The main cause of breast cancer is the deterioration of malignant tumor cells in breast tissue. Early diagnosis of tumors has become the most effective way to prevent breast cancer. Method: For distinguishing between tumor and non-tumor in MRI, a new type of computer-aided detection CAD system for breast tumors is designed in this paper. The CAD system was constructed using three networks, namely, the VGG16, Inception V3, and ResNet50. Then, the influence of the convolutional neural network second migration on the experimental results was further explored in the VGG16 system. Result: CAD system built based… More >

T. Gopalakrishnan¹, Periakaruppan Sudhakaran², K. C. Ramya³, K. Sathesh Kumar⁴, Fahd N. Al-Wesabi^5,6,*, Manal Abdullah Alohali⁷, Anwer Mustafa Hilal⁸

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 305-320, 2022, DOI:10.32604/cmc.2022.020914 - 03 November 2021

Abstract Muscular Dystrophy (MD) is a group of inherited muscular diseases that are commonly diagnosed with the help of techniques such as muscle biopsy, clinical presentation, and Muscle Magnetic Resonance Imaging (MRI). Among these techniques, Muscle MRI recommends the diagnosis of muscular dystrophy through identification of the patterns that exist in muscle fatty replacement. But the patterns overlap among various diseases whereas there is a lack of knowledge prevalent with regards to disease-specific patterns. Therefore, artificial intelligence techniques can be used in the diagnosis of muscular dystrophies, which enables us to analyze, learn, and predict for… More >

R. Shijitha^1,*, P. Karthigaikumar², A. Stanly Paul²

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5233-5249, 2022, DOI:10.32604/cmc.2022.020227 - 11 October 2021

Abstract Brain Hemorrhagic stroke is a serious malady that is caused by the drop in blood flow through the brain and causes the brain to malfunction. Precise segmentation of brain hemorrhage is crucial, so an enhanced segmentation is carried out in this research work. The brain image of various patients has taken using an MRI scanner by the utilization of T1, T2, and FLAIR sequence. This work aims to segment the Brain Hemorrhagic stroke using deep learning-based Multi-resolution UNet (multires UNet) through morphological operations. It is hard to precisely segment the brain lesions to extract the More >

Liqiong Ding¹, Pingsheng Li², Liu He¹, Fengnan Xu¹, Jieqiong Ding^3,*, Binhua Luo^1,*

Journal of Renewable Materials, Vol.10, No.3, pp. 767-780, 2022, DOI:10.32604/jrm.2022.016903 - 28 September 2021

Abstract The development of contrast agents that can be activated by multiple modes is of great significance for tumor diagnosis. In this study, the lactoferrin (Lf)-conjugated polylactic acid (PLLA) nanobubbles (Lf-PLLA NBs) were used to encapsulate liquid perfluoropentane (PFP) with the double emulsion method, creating PFP loaded (PFP/Lf-PLLA) NBs for the ultrasound/magnetic resonance dual-modality imaging of subcutaneous tumor. The particle diameter and stability of nanobubbles were investigated by photon correlation spectroscopy. The biocompatibility of nanobubbles was preliminarily evaluated by cell proliferation and migration assay, hemolysis rate, and blood biochemistry analysis. A B-mode clinical ultrasound real-time imaging… More >

Samuel Helrich¹, Wesley Pate¹, Nishant Garg¹, Philip Barbosa², Shaun Wason¹

Canadian Journal of Urology, Vol.28, No.6, pp. 10889-10899, 2021

Abstract Introduction: To compare pelvic ultrasound (PUS) and transrectal ultrasound (TRUS) to magnetic resonance imaging (MRI) in the estimation of prostate size.
Materials and methods: After IRB approval, we performed a single-center, retrospective study of 91 patients who had prostate sizing between August 2013 and June 2017. Correlation, reliability, and agreement between PUS, TRUS, and MRI were calculated through the Pearson coefficient, intraclass correlation coefficient, and Bland-Altman analysis, respectively. Data was stratified by prostate size, body mass index, and time between imaging acquisition.
Results: A total of 91 patients underwent all three imaging methods. Median age was 64, median… More >

Jing Peng^1,2,4, Chaoyang Xia², Yuanwei Xu³, Xiaojie Li², Xi Wu², Xiao Han^1,4, Xinlai Chen⁵, Yucheng Chen³, Zhe Cui^1,4,*

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 259-272, 2021, DOI:10.32604/iasc.2021.016749 - 26 July 2021

Abstract Cardiomyopathy is a group of diseases that affect the heart and can cause serious health problems. Segmentation and classification are important for automating the clinical diagnosis and treatment planning for cardiomyopathy. However, this automation is difficult because of the poor quality of cardiac magnetic resonance (CMR) imaging data and varying dimensions caused by movement of the ventricle. To address these problems, a deep multi-task framework based on a convolutional neural network (CNN) is proposed to segment the left ventricle (LV) myocardium and classify cardiopathy simultaneously. The proposed model consists of a longitudinal encoder–decoder structure that… More >

Mohamed Abdel-Basset¹, Reda Mohamed¹, Mohamed Abouhawwash^2,3, Ripon K. Chakrabortty⁴, Michael J. Ryan⁴, Yunyoung Nam^5,*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2961-2977, 2021, DOI:10.32604/cmc.2021.016956 - 06 May 2021

Abstract Image segmentation is vital when analyzing medical images, especially magnetic resonance (MR) images of the brain. Recently, several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation; however, the algorithms become trapped in local minima and have low convergence speeds, particularly as the number of threshold levels increases. Consequently, in this paper, we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm (JSA) (an optimizer). We modify the JSA to prevent descents into local minima, and we accelerate convergence toward optimal solutions. The improvement is… More >