Khalil Ibrahim Lairedj¹, Zouaoui Chama¹, Amina Bagdaoui¹, Samia Larguech², Younes Menni^3,4,*, Nidhal Becheikh⁵, Lioua Kolsi^6,*, Badr M. Alshammari⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2419-2443, 2025, DOI:10.32604/cmes.2025.069396 - 31 August 2025

Abstract Brain tumor segmentation from Magnetic Resonance Imaging (MRI) supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans, making it a crucial yet challenging task. Supervised models such as 3D U-Net perform well in this domain, but their accuracy significantly improves with appropriate preprocessing. This paper demonstrates the effectiveness of preprocessing in brain tumor segmentation by applying a pre-segmentation step based on the Generalized Gaussian Mixture Model (GGMM) to T1 contrast-enhanced MRI scans from the BraTS 2020 dataset. The Expectation-Maximization (EM) algorithm is employed to estimate parameters for four tissue classes, generating a More >

Yao-Tien Chen¹, Nisar Ahmad^1,*, Khursheed Aurangzeb²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1197-1224, 2025, DOI:10.32604/cmes.2025.066580 - 31 July 2025

Abstract Accurate and efficient brain tumor segmentation is essential for early diagnosis, treatment planning, and clinical decision-making. However, the complex structure of brain anatomy and the heterogeneous nature of tumors present significant challenges for precise anomaly detection. While U-Net-based architectures have demonstrated strong performance in medical image segmentation, there remains room for improvement in feature extraction and localization accuracy. In this study, we propose a novel hybrid model designed to enhance 3D brain tumor segmentation. The architecture incorporates a 3D ResNet encoder known for mitigating the vanishing gradient problem and a 3D U-Net decoder. Additionally, to… More > Graphic Abstract

Elias Hossain¹, Md. Shazzad Hossain², Md. Selim Hossain³, Sabila Al Jannat⁴, Moontahina Huda⁵, Sameer Alsharif⁶, Osama S. Faragallah⁷, Mahmoud M. A. Eid⁸, Ahmed Nabih Zaki Rashed^9,*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4509-4523, 2022, DOI:10.32604/cmc.2022.025977 - 21 April 2022

Abstract Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional Magnetic Resonance Image (MRI) and Computed Tomography (CT) scans utilizing 3D U-Net Design and ResNet50, taken after by conventional classification strategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, and the 3D U-Net scored 97.99% among the different methods of deep learning. It is to be mentioned that traditional Convolutional Neural Network (CNN) gives 97.90% accuracy on top of the 3D MRI. In expansion, the image fusion approach combines the multimodal images and makes a fused… More >

Juhong Tie^1,2,*, Hui Peng², Jiliu Zhou^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 427-445, 2021, DOI:10.32604/cmes.2021.014107 - 22 July 2021

Abstract The main task of magnetic resonance imaging (MRI) automatic brain tumor segmentation is to automatically segment the brain tumor edema, peritumoral edema, endoscopic core, enhancing tumor core and nonenhancing tumor core from 3D MR images. Because the location, size, shape and intensity of brain tumors vary greatly, it is very difficult to segment these brain tumor regions automatically. In this paper, by combining the advantages of DenseNet and ResNet, we proposed a new 3D U-Net with dense encoder blocks and residual decoder blocks. We used dense blocks in the encoder part and residual blocks in… More >