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 >

Yuchun Li^1,4, Mengxing Huang^1,*, 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 >

Elisa Listo^1,#, Nicola Martini^2,#, Stefano Salvadori³, Elisa Valenti³, Nicola Stagnaro¹, Gianluca Trocchio⁴, Chiara Marrone⁵, Alberto Clemente⁶, Francesca Raimondi^7,*, Pierluigi Festa⁵, Lamia Ait Ali^5,8,*

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

S. Kannan^1,*, 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 >

Liwei Deng¹, Henan Sun¹, Jing Wang², Sijuan Huang³, Xin Yang^3,*

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 >

Tawfeeq Shawly¹, Ahmed Alsheikhy^2,*

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 >

Nerejda Shehu^1,*, Christian Meierhofer¹, Anja Hennemuth^2,3, Markus Hüllebrand^2,3, 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

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 >

Umair Muneer Butt^1,2,*, Rimsha Arif², Sukumar Letchmunan^1,*, 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 >

Abdul Haseeb¹, Muhammad Attique Khan^1,*, 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 >