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Search Results (102)
  • Open Access

    ARTICLE

    Transformation of MRI Images to Three-Level Color Spaces for Brain Tumor Classification Using Deep-Net

    Fadl Dahan*

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 381-395, 2024, DOI:10.32604/iasc.2024.047921

    Abstract In the domain of medical imaging, the accurate detection and classification of brain tumors is very important. This study introduces an advanced method for identifying camouflaged brain tumors within images. Our proposed model consists of three steps: Feature extraction, feature fusion, and then classification. The core of this model revolves around a feature extraction framework that combines color-transformed images with deep learning techniques, using the ResNet50 Convolutional Neural Network (CNN) architecture. So the focus is to extract robust feature from MRI images, particularly emphasizing weighted average features extracted from the first convolutional layer renowned for their discriminative power. To enhance… More >

  • Open Access

    ARTICLE

    Intelligent Machine Learning Based Brain Tumor Segmentation through Multi-Layer Hybrid U-Net with CNN Feature Integration

    Sharaf J. Malebary*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1301-1317, 2024, DOI:10.32604/cmc.2024.047917

    Abstract Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates. Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitating the development of more precise and efficient methodologies. To address this formidable challenge, we propose an advanced approach for segmenting brain tumor Magnetic Resonance Imaging (MRI) images that harnesses the formidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methods have displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, marked by irregular shapes, varying sizes, uneven distribution, and limited available… More >

  • Open Access

    ARTICLE

    Attention Guided Multi Scale Feature Fusion Network for Automatic Prostate Segmentation

    Yuchun Li1,4, Mengxing Huang1,*, Yu Zhang2, Zhiming Bai3

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1649-1668, 2024, DOI:10.32604/cmc.2023.046883

    Abstract The precise and automatic segmentation of prostate magnetic resonance imaging (MRI) images is vital for assisting doctors in diagnosing prostate diseases. In recent years, many advanced methods have been applied to prostate segmentation, but due to the variability caused by prostate diseases, automatic segmentation of the prostate presents significant challenges. In this paper, we propose an attention-guided multi-scale feature fusion network (AGMSF-Net) to segment prostate MRI images. We propose an attention mechanism for extracting multi-scale features, and introduce a 3D transformer module to enhance global feature representation by adding it during the transition phase from encoder to decoder. In the… More >

  • Open Access

    ARTICLE

    Comparison of 2D and 4D Flow MRI Measurements for Hemodynamic Evaluation of the Fontan Palliation

    Elisa Listo1,#, Nicola Martini2,#, Stefano Salvadori3, Elisa Valenti3, Nicola Stagnaro1, Gianluca Trocchio4, Chiara Marrone5, Alberto Clemente6, Francesca Raimondi7,*, Pierluigi Festa5, Lamia Ait Ali5,8,*

    Congenital Heart Disease, Vol.18, No.6, pp. 627-638, 2023, DOI:10.32604/chd.2023.030312

    Abstract Background: The assessment of Fontan circuit’s flow is traditionally evaluated by multiple through-plane phase-contrast MRI acquisitions (2D flow), while recently, a single volumetric 4D-flow MRI acquisition is emerging as a comprehensive tool for the hemodynamic evaluation in congenital heart diseases. Purpose: To compare 2D and 4D-flow MRI measurements in patients after Fontan palliation and to evaluate parameters affecting potential disagreement. Methods: 39 patients after Fontan palliation (23 males, age 22 ± 11 years) who underwent cardiac MRI with 2D and 4D-flow MRI acquisition were included in the study. In all patients, blood flow quantification in the Fontan circuit and aorta… More > Graphic Abstract

    Comparison of 2D and 4D Flow MRI Measurements for Hemodynamic Evaluation of the Fontan Palliation

  • Open Access

    ARTICLE

    Advancing Brain Tumor Analysis through Dynamic Hierarchical Attention for Improved Segmentation and Survival Prognosis

    S. Kannan1,*, S. Anusuya2

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3835-3851, 2023, DOI:10.32604/cmc.2023.042465

    Abstract Gliomas, the most prevalent primary brain tumors, require accurate segmentation for diagnosis and risk assessment. In this paper, we develop a novel deep learning-based method, the Dynamic Hierarchical Attention for Improved Segmentation and Survival Prognosis (DHA-ISSP) model. The DHA-ISSP model combines a three-band 3D convolutional neural network (CNN) U-Net architecture with dynamic hierarchical attention mechanisms, enabling precise tumor segmentation and survival prediction. The DHA-ISSP model captures fine-grained details and contextual information by leveraging attention mechanisms at multiple levels, enhancing segmentation accuracy. By achieving remarkable results, our approach surpasses 369 competing teams in the 2020 Multimodal Brain Tumor Segmentation Challenge. With… More >

  • Open Access

    ARTICLE

    A Novel Unsupervised MRI Synthetic CT Image Generation Framework with Registration Network

    Liwei Deng1, Henan Sun1, Jing Wang2, Sijuan Huang3, Xin Yang3,*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2271-2287, 2023, DOI:10.32604/cmc.2023.039062

    Abstract In recent years, radiotherapy based only on Magnetic Resonance (MR) images has become a hot spot for radiotherapy planning research in the current medical field. However, functional computed tomography (CT) is still needed for dose calculation in the clinic. Recent deep-learning approaches to synthesized CT images from MR images have raised much research interest, making radiotherapy based only on MR images possible. In this paper, we proposed a novel unsupervised image synthesis framework with registration networks. This paper aims to enforce the constraints between the reconstructed image and the input image by registering the reconstructed image with the input image… More >

  • Open Access

    ARTICLE

    Classification of Brain Tumors Using Hybrid Feature Extraction Based on Modified Deep Learning Techniques

    Tawfeeq Shawly1, Ahmed Alsheikhy2,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 425-443, 2023, DOI:10.32604/cmc.2023.040561

    Abstract According to the World Health Organization (WHO), Brain Tumors (BrT) have a high rate of mortality across the world. The mortality rate, however, decreases with early diagnosis. Brain images, Computed Tomography (CT) scans, Magnetic Resonance Imaging scans (MRIs), segmentation, analysis, and evaluation make up the critical tools and steps used to diagnose brain cancer in its early stages. For physicians, diagnosis can be challenging and time-consuming, especially for those with little expertise. As technology advances, Artificial Intelligence (AI) has been used in various domains as a diagnostic tool and offers promising outcomes. Deep-learning techniques are especially useful and have achieved… 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

    SC-Net: A New U-Net Network for Hippocampus Segmentation

    Xinyi Xiao, Dongbo Pan*, Jianjun Yuan

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3179-3191, 2023, DOI:10.32604/iasc.2023.041208

    Abstract Neurological disorders like Alzheimer’s disease have a significant impact on the lives and health of the elderly as the aging population continues to grow. Doctors can achieve effective prevention and treatment of Alzheimer’s disease according to the morphological volume of hippocampus. General segmentation techniques frequently fail to produce satisfactory results due to hippocampus’s small size, complex structure, and fuzzy edges. We develop a new SC-Net model using complete brain MRI images to achieve high-precision segmentation of hippocampal structures. The proposed network improves the accuracy of hippocampal structural segmentation by retaining the original location information of the hippocampus. Extensive experimental results… More >

  • Open Access

    ARTICLE

    Feature Enhanced Stacked Auto Encoder for Diseases Detection in Brain MRI

    Umair Muneer Butt1,2,*, Rimsha Arif2, Sukumar Letchmunan1,*, Babur Hayat Malik2, Muhammad Adil Butt2

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2551-2570, 2023, DOI:10.32604/cmc.2023.039164

    Abstract The detection of brain disease is an essential issue in medical and research areas. Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging (MRI) images. These techniques involve training neural networks on large datasets of MRI images, allowing the networks to learn patterns and features indicative of different brain diseases. However, several challenges and limitations still need to be addressed further to improve the accuracy and effectiveness of these techniques. This paper implements a Feature Enhanced Stacked Auto Encoder (FESAE) model to detect brain diseases. The standard stack auto encoder’s results are… More >

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