Shaopeng Li, Peng Wang, Jun Qiu, Yiju Xie, Dawei Yin, Kexue Deng^*

Oncologie, Vol.24, No.3, pp. 527-538, 2022, DOI:10.32604/oncologie.2022.022815 - 19 September 2022

Abstract Background: To compare the differential diagnostic value of iterative decomposition of water and fat with the echo asymmetrical and least-squares estimation quantitation sequence (IDEAL-IQ) with that of intravoxel incoherent motion diffusion-weighted imaging (IVIM DWI) in differentiating between alpha fetoprotein (AFP)-negative hepatocellular carcinoma (HCC) and focal nodular hyperplasia (FNH). Materials and Methods: A total of 28 AFP-negative HCC cases and 15 FNH cases were scanned using the IDEAL-IQ and IVIM-DWI magnetic resonance imaging (MRI) protocols. Two radiologists independently assessed the fat fraction (FF) and the iron level surrogate (R2*) derived from the IDEAL-IQ images and the apparent diffusion… More >

Muhammad Ali¹, Jamal Hussain Shah¹, Muhammad Attique Khan², Majed Alhaisoni³, Usman Tariq⁴, Tallha Akram⁵, Ye Jin Kim⁶, Byoungchol Chang^7,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4501-4518, 2022, DOI:10.32604/cmc.2022.030392 - 28 July 2022

Abstract Tumor detection has been an active research topic in recent years due to the high mortality rate. Computer vision (CV) and image processing techniques have recently become popular for detecting tumors in MRI images. The automated detection process is simpler and takes less time than manual processing. In addition, the difference in the expanding shape of brain tumor tissues complicates and complicates tumor detection for clinicians. We proposed a new framework for tumor detection as well as tumor classification into relevant categories in this paper. For tumor segmentation, the proposed framework employs the Particle Swarm… More >

Gregory Perens^1,*, Takegawa Yoshida², J. Paul Finn²

Congenital Heart Disease, Vol.17, No.4, pp. 387-392, 2022, DOI:10.32604/chd.2022.021233 - 04 July 2022

Abstract An infant male presented with the rare anatomy consisting of situs solitus, concordant atrioventricular connections to L-looped ventricles, double outlet right ventricle (DORV), and hypoplastic aortic arch. 6 months after neonatal aortic arch repair, the morphologic right ventricle function deteriorated, and surgical evaluation was undertaken to determine if either biventricular repair with a systemic morphologic left ventricle or right ventricular exclusion was possible. After initial echocardiography, magnetic resonance imaging (MRI) was used to create detailed axial and 4-dimensional (4D) images and 3-dimensional (3D) printed models. The detailed anatomy of this rare, complex case and its More > Graphic Abstract

Irfan Ahmed Usmani^1,*, Muhammad Tahir Qadri¹, Razia Zia¹, Asif Aziz², Farheen Saeed³

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4369-4392, 2022, DOI:10.32604/cmc.2022.030698 - 16 June 2022

Abstract Knowledge-based transfer learning techniques have shown good performance for brain tumor classification, especially with small datasets. However, to obtain an optimized model for targeted brain tumor classification, it is challenging to select a pre-trained deep learning (DL) model, optimal values of hyperparameters, and optimization algorithm (solver). This paper first presents a brief review of recent literature related to brain tumor classification. Secondly, a robust framework for implementing the transfer learning technique is proposed. In the proposed framework, a Cartesian product matrix is generated to determine the optimal values of the two important hyperparameters: batch size… More >

B. Priestly Shan¹, O. Jeba Shiney¹, Sharzeel Saleem², V. Rajinikanth³, Atef Zaguia⁴, Dilbag Singh^5,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2749-2763, 2022, DOI:10.32604/cmc.2022.023057 - 29 March 2022

Abstract Low contrast of Magnetic Resonance (MR) images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis. State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images. Drastic changes in brightness features, induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings. To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well. This method termed as Power-law and Logarithmic Modification-based Histogram Equalization (PLMHE) partitions the histogram More >

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 >