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  • Open Access

    REVIEW

    Congenital Absence of Pericardium: The Largest Systematic Review in the Field on 247 Worldwide Cases (1977-Now)

    Pier Paolo Bassareo1,2,3,*, Aurelio Secinaro4, Paolo Ciliberti5, Massimo Chessa6,7, Marco Alfonso Perrone5,8, Kevin Patrick Walsh1,2,3, Colin Joseph Mcmahon2,3

    Congenital Heart Disease, Vol.18, No.6, pp. 595-610, 2023, DOI:10.32604/chd.2023.046229

    Abstract Background: Congenital absence of pericardium (CAP), also known as pericardial agenesis, represents an uncommon cardiac abnormality and mostly incidental finding. It can be subdivided into complete and partial (left or right-sided) forms. Because of its infrequency, just case reports and a few case series have been released so far. This paper represents the largest systematic review in the field. Nine features (age at diagnosis, type, gender, clinical presentation, electrocardiography, imaging (ultrasounds, CT/MRI), concomitant cardiac defects, and outcome) were analysed. Methods: The electronic database PubMed was investigated from its establishment up to July 15th, 2023. Just case reports and case series… More >

  • Open Access

    ARTICLE

    3D Kronecker Convolutional Feature Pyramid for Brain Tumor Semantic Segmentation in MR Imaging

    Kainat Nazir1, Tahir Mustafa Madni1, Uzair Iqbal Janjua1, Umer Javed2, Muhammad Attique Khan3, Usman Tariq4, Jae-Hyuk Cha5,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2861-2877, 2023, DOI:10.32604/cmc.2023.039181

    Abstract Brain tumor significantly impacts the quality of life and changes everything for a patient and their loved ones. Diagnosing a brain tumor usually begins with magnetic resonance imaging (MRI). The manual brain tumor diagnosis from the MRO images always requires an expert radiologist. However, this process is time-consuming and costly. Therefore, a computerized technique is required for brain tumor detection in MRI images. Using the MRI, a novel mechanism of the three-dimensional (3D) Kronecker convolution feature pyramid (KCFP) is used to segment brain tumors, resolving the pixel loss and weak processing of multi-scale lesions. A single dilation rate was replaced… More >

  • Open Access

    ARTICLE

    Minor Pressure Differences within the Fontan-Anastomosis in Patients with Total Cavopulmonary Connection by 4D-Flow Magnetic Resonance Imaging

    Nerejda Shehu1,*, Christian Meierhofer1, Anja Hennemuth2,3, Markus Hüllebrand2,3, Pavlo Yevtushenko3, Peter Ewert1, Stefan Martinoff4, Heiko Stern1

    Congenital Heart Disease, Vol.18, No.4, pp. 461-474, 2023, DOI:10.32604/chd.2023.031075

    Abstract Background: Pressure measurement in total cavopulmonary connection (TCPC) patients is a domain of cardiac catheterization. 4D velocity encoded cardiovascular magnetic resonance (4D–flow MRI) offers an alternative for assessment of even minor pressure differences. The scope of this study was to measure even minor pressure differences in the anastomosis of TCPC patients, who are clinically uncompromised. Methods: Twenty-four patients (median 15 years [8;34]) with TCPC were studied prospectively by 4D-flow MRI. Pressure differences between superior vena cava (SVC) and extracardiac conduit (C) to both right pulmonary artery (RPA) and left pulmonary artery (LPA) were assessed. Small fluid obstructions as vortices within… More > Graphic Abstract

    Minor Pressure Differences within the Fontan-Anastomosis in Patients with Total Cavopulmonary Connection by 4D-Flow Magnetic Resonance Imaging

  • Open Access

    ARTICLE

    Knee Osteoarthritis Classification Using X-Ray Images Based on Optimal Deep Neural Network

    Abdul Haseeb1, Muhammad Attique Khan1,*, Faheem Shehzad1, Majed Alhaisoni2, Junaid Ali Khan1, Taerang Kim3, Jae-Hyuk Cha3

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2397-2415, 2023, DOI:10.32604/csse.2023.040529

    Abstract X-Ray knee imaging is widely used to detect knee osteoarthritis due to ease of availability and lesser cost. However, the manual categorization of knee joint disorders is time-consuming, requires an expert person, and is costly. This article proposes a new approach to classifying knee osteoarthritis using deep learning and a whale optimization algorithm. Two pre-trained deep learning models (Efficientnet-b0 and Densenet201) have been employed for the training and feature extraction. Deep transfer learning with fixed hyperparameter values has been employed to train both selected models on the knee X-Ray images. In the next step, fusion is performed using a canonical… More >

  • Open Access

    ARTICLE

    Optimized Three-Dimensional Cardiovascular Magnetic Resonance Whole Heart Imaging Utilizing Non-Selective Excitation and Compressed Sensing in Children and Adults with Congenital Heart Disease

    Ingo Paetsch1,*, Roman Gebauer2, Christian Paech2, Frank-Thomas Riede2, Sabrina Oebel1, Andreas Bollmann1, Christian Stehning3, Jouke Smink4, Ingo Daehnert2, Cosima Jahnke1

    Congenital Heart Disease, Vol.18, No.3, pp. 279-294, 2023, DOI:10.32604/chd.2023.029634

    Abstract Background: In congenital heart disease (CHD) patients, detailed three-dimensional anatomy depiction plays a pivotal role for diagnosis and therapeutical decision making. Hence, the present study investigated the applicability of an advanced cardiovascular magnetic resonance (CMR) whole heart imaging approach utilizing nonselective excitation and compressed sensing for anatomical assessment and interventional guidance of CHD patients in comparison to conventional dynamic CMR angiography. Methods: 86 consecutive pediatric patients and adults with congenital heart disease (age, 1 to 74 years; mean, 35 years) underwent CMR imaging including a free-breathing, ECG-triggered 3D nonselective SSFP whole heart acquisition using compressed SENSE (nsWHcs). Anatomical assessability and… More >

  • Open Access

    ARTICLE

    IRMIRS: Inception-ResNet-Based Network for MRI Image Super-Resolution

    Wazir Muhammad1, Zuhaibuddin Bhutto2,*, Salman Masroor3,4, Murtaza Hussain Shaikh5, Jalal Shah2, Ayaz Hussain1

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1121-1142, 2023, DOI:10.32604/cmes.2023.021438

    Abstract Medical image super-resolution is a fundamental challenge due to absorption and scattering in tissues. These challenges are increasing the interest in the quality of medical images. Recent research has proven that the rapid progress in convolutional neural networks (CNNs) has achieved superior performance in the area of medical image super-resolution. However, the traditional CNN approaches use interpolation techniques as a preprocessing stage to enlarge low-resolution magnetic resonance (MR) images, adding extra noise in the models and more memory consumption. Furthermore, conventional deep CNN approaches used layers in series-wise connection to create the deeper mode, because this later end layer cannot… More >

  • Open Access

    ARTICLE

    Lung Cancer Segmentation with Three-Parameter Logistic Type Distribution Model

    Debnath Bhattacharyya1, Eali. Stephen Neal Joshua2, N. Thirupathi Rao2, Yung-cheol Byun3,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1447-1465, 2023, DOI:10.32604/cmc.2023.031878

    Abstract Lung cancer is the leading cause of mortality in the world affecting both men and women equally. When a radiologist just focuses on the patient’s body, it increases the amount of strain on the radiologist and the likelihood of missing pathological information such as abnormalities are increased. One of the primary objectives of this research work is to develop computer-assisted diagnosis and detection of lung cancer. It also intends to make it easier for radiologists to identify and diagnose lung cancer accurately. The proposed strategy which was based on a unique image feature, took into consideration the spatial interaction of… More >

  • Open Access

    ARTICLE

    Automated Brain Hemorrhage Classification and Volume Analysis

    Maryam Wardah1, Muhammad Mateen1,*, Tauqeer Safdar Malik2, Mohammad Eid Alzahrani3, Adil Fahad3, Abdulmohsen Almalawi4, Rizwan Ali Naqvi5

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2283-2299, 2023, DOI:10.32604/cmc.2023.030706

    Abstract Brain hemorrhage is a serious and life-threatening condition. It can cause permanent and lifelong disability even when it is not fatal. The word hemorrhage denotes leakage of blood within the brain and this leakage of blood from capillaries causes stroke and adequate supply of oxygen to the brain is hindered. Modern imaging methods such as computed tomography (CT) and magnetic resonance imaging (MRI) are employed to get an idea regarding the extent of the damage. An early diagnosis and treatment can save lives and limit the adverse effects of a brain hemorrhage. In this case, a deep neural network (DNN)… More >

  • Open Access

    ARTICLE

    Xception-Fractalnet: Hybrid Deep Learning Based Multi-Class Classification of Alzheimer’s Disease

    Mudiyala Aparna, Battula Srinivasa Rao*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6909-6932, 2023, DOI:10.32604/cmc.2023.034796

    Abstract Neurological disorders such as Alzheimer’s disease (AD) are very challenging to treat due to their sensitivity, technical challenges during surgery, and high expenses. The complexity of the brain structures makes it difficult to distinguish between the various brain tissues and categorize AD using conventional classification methods. Furthermore, conventional approaches take a lot of time and might not always be precise. Hence, a suitable classification framework with brain imaging may produce more accurate findings for early diagnosis of AD. Therefore in this paper, an effective hybrid Xception and Fractalnet-based deep learning framework are implemented to classify the stages of AD into… More >

  • Open Access

    ARTICLE

    Machine Learning-Based Models for Magnetic Resonance Imaging (MRI)-Based Brain Tumor Classification

    Abdullah A. Asiri1, Bilal Khan2, Fazal Muhammad3,*, Shams ur Rahman4, Hassan A. Alshamrani1, Khalaf A. Alshamrani1, Muhammad Irfan5, Fawaz F. Alqhtani1

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 299-312, 2023, DOI:10.32604/iasc.2023.032426

    Abstract In the medical profession, recent technological advancements play an essential role in the early detection and categorization of many diseases that cause mortality. The technique rising on daily basis for detecting illness in magnetic resonance through pictures is the inspection of humans. Automatic (computerized) illness detection in medical imaging has found you the emergent region in several medical diagnostic applications. Various diseases that cause death need to be identified through such techniques and technologies to overcome the mortality ratio. The brain tumor is one of the most common causes of death. Researchers have already proposed various models for the classification… More >

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