Novsheena Rasool^1,*, Javaid Iqbal Bhat¹, Najib Ben Aoun^2,3, Abdullah Alharthi⁴, Niyaz Ahmad Wani⁵, Vikram Chopra⁶, Muhammad Shahid Anwar^7,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 885-909, 2024, DOI:10.32604/cmc.2024.055900 - 15 October 2024

Abstract Gliomas are aggressive brain tumors known for their heterogeneity, unclear borders, and diverse locations on Magnetic Resonance Imaging (MRI) scans. These factors present significant challenges for MRI-based segmentation, a crucial step for effective treatment planning and monitoring of glioma progression. This study proposes a novel deep learning framework, ResNet Multi-Head Attention U-Net (ResMHA-Net), to address these challenges and enhance glioma segmentation accuracy. ResMHA-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention mechanisms. This powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture… More >

Mahesh Thyluru Ramakrishna¹, Kuppusamy Pothanaicker², Padma Selvaraj³, Surbhi Bhatia Khan^4,7,*, Vinoth Kumar Venkatesan⁵, Saeed Alzahrani⁶, Mohammad Alojail⁶

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 867-883, 2024, DOI:10.32604/cmc.2024.053563 - 15 October 2024

Abstract Brain tumor is a global issue due to which several people suffer, and its early diagnosis can help in the treatment in a more efficient manner. Identifying different types of brain tumors, including gliomas, meningiomas, pituitary tumors, as well as confirming the absence of tumors, poses a significant challenge using MRI images. Current approaches predominantly rely on traditional machine learning and basic deep learning methods for image classification. These methods often rely on manual feature extraction and basic convolutional neural networks (CNNs). The limitations include inadequate accuracy, poor generalization of new data, and limited ability… More >

Bing Li^1,2,*, Liangyu Wang¹, Xia Liu^1,2, Hongbin Fan¹, Bo Wang³, Shoudi Tong¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1543-1561, 2024, DOI:10.32604/cmc.2024.052009 - 18 July 2024

Abstract Nuclear magnetic resonance imaging of breasts often presents complex backgrounds. Breast tumors exhibit varying sizes, uneven intensity, and indistinct boundaries. These characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor segmentation. Thus, we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention mechanisms. Initially, the breast region of interest is extracted to isolate the breast area from surrounding tissues and organs. Subsequently, we devise a fusion network incorporating multi-scale features and boundary attention mechanisms for breast tumor segmentation. We incorporate multi-scale parallel dilated convolution modules into… More >

Fadl Dahan^*

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 381-395, 2024, DOI:10.32604/iasc.2024.047921 - 21 May 2024

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… More >

Sharaf J. Malebary^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1301-1317, 2024, DOI:10.32604/cmc.2024.047917 - 25 April 2024

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,… 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 - 27 February 2024

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 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 - 19 January 2024

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… 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 - 26 December 2023

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… 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 - 29 November 2023

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… More >

Tawfeeq Shawly¹, Ahmed Alsheikhy^2,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 425-443, 2023, DOI:10.32604/cmc.2023.040561 - 31 October 2023

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… More >