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

Amarendra Reddy Panyala^1,2, M. Baskar^3,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3883-3899, 2023, DOI:10.32604/csse.2023.037050 - 03 April 2023

Abstract The deep learning models are identified as having a significant impact on various problems. The same can be adapted to the problem of brain tumor classification. However, several deep learning models are presented earlier, but they need better classification accuracy. An efficient Multi-Feature Approximation Based Convolution Neural Network (CNN) model (MFA-CNN) is proposed to handle this issue. The method reads the input 3D Magnetic Resonance Imaging (MRI) images and applies Gabor filters at multiple levels. The noise-removed image has been equalized for its quality by using histogram equalization. Further, the features like white mass, grey… More >

Ibrahim Mahmoud El-Henawy¹, Mostafa Elbaz², Zainab H. Ali^3,*, Noha Sakr⁴

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3489-3502, 2023, DOI:10.32604/cmc.2023.033356 - 31 October 2022

Abstract Medical image segmentation is a crucial process for computer-aided diagnosis and surgery. Medical image segmentation refers to portioning the images into small, disjointed parts for simplifying the processes of analysis and examination. Rician and speckle noise are different types of noise in magnetic resonance imaging (MRI) that affect the accuracy of the segmentation process negatively. Therefore, image enhancement has a significant role in MRI segmentation. This paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free image.… More >