Aug 2026· Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi· Vol 43 4, pp.
828-835
· 0 citations
Medicine
TL;DR
DRCSA-Net achieves high accuracy on all four public datasets and demonstrates good effectiveness and stability in the single-/dual-path comparison, ablation experiments, and robustness analysis, providing a structurally clear and stable implementation scheme for brain tumor MRI classification.
Magnetic Resonance Imaging (MRI) is very crucial in the diagnosis and examination of brain tumors because it can give good images of the soft tissues and anatomy. Nonetheless, the detection and localization of the tumor areas are still difficult due to differences in tumor shape, size, and pattern of intensity change...
Arshad Hashmi, Muzammil Hussain, Anas W. Abulfarj et al.· Scientific Reports· 0 citations
Experimental results on the BraTS 2021 and BraTS-Africa datasets demonstrate that DuS-Net achieves higher segmentation accuracy than recent mainstream segmentation methods, validating its effectiveness for complex brain glioma segmentation and indicating its potential value for computer-assisted glioma segmentation.
Zhe Du, Tian Tang, Kepeng Yang et al.· IEEE Access· 0 citations
A novel convolutional neural network architecture for efficient tumor classification by integrating multi-scale fusion techniques with dual-path attention is presented, making it a great fit for resource-constrained medical devices that need to be deployed in real-time.
S. P. Praveen, Shaik Salma Begum, P. Padmavathi et al.· Journal of Advances in Infor...· 0 citations
Alzheimer's disease (AD) classification using magnetic resonance imaging (MRI) is essential for early clinical intervention. However, deep learning approaches often face challenges such as limited medical data, high intraclass variability, and uneven diagnostic contributions from individual MRI slices. To address these...
The proposed framework is intended to support clinical image assessment and prioritization rather than replace expert diagnosis, and demonstrates the potential of hybrid CNN-Transformer architectures for robust and scalable computer-assisted multi-disease screening from medical imaging data.
M. Balakrishnan, K. Ananthi, S. R. et al.· International journal of com...· 0 citations
Evaluation on the BraTS GOAT validation dataset demonstrated that the proposed Multi-Stage Dynamic Prompt nnU-Net outperformed the baseline nnU Net across the majority of evaluated metrics and tumor subregions.
Mohammad Mahdi Danesh Pajouh, Sara Saeedi· 0 citations
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