Published: July 11, 2024
Language: Английский
Published: July 11, 2024
Language: Английский
Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104025 - 104025
Published: Jan. 1, 2025
Language: Английский
Citations
3Digital Health, Journal Year: 2025, Volume and Issue: 11
Published: Jan. 1, 2025
Objective Accurate segmentation of brain tumors in medical imaging is essential for diagnosis and treatment planning. Current techniques often struggle with capturing complex tumor features are computationally demanding, limiting their clinical application. This study introduces the attention-based convolutional U-Net (ACU-Net) model, designed to improve accuracy efficiency fMRI images by incorporating attention mechanisms that selectively highlight critical while preserving spatial context. Methods The ACU-Net model combines neural networks (CNNs) enhance feature extraction coherence. We evaluated on BraTS 2018 2020 datasets using rigorous data splitting training, validation, testing. Performance metrics, particularly Dice scores, were used assess across different regions, including whole (WT), core (TC), enhancing (ET) classes. Results demonstrated high accuracy, achieving scores 99.23%, 99.27%, 96.99% WT, TC, ET, respectively, dataset, 98.72%, 98.40%, 97.66% ET dataset. These results indicate effectively captures boundaries subregions precision, surpassing traditional approaches. Conclusion shows significant potential planning providing precise efficient images. integration within a CNN architecture proves beneficial identifying structures, suggesting can be valuable tool applications.
Language: Английский
Citations
2Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 100, P. 107027 - 107027
Published: Oct. 24, 2024
Language: Английский
Citations
7Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 189, P. 109916 - 109916
Published: March 6, 2025
Language: Английский
Citations
1Published: Jan. 2, 2025
Traditional approaches to segmenting retinal vessels, brain tumors, and inner ear structures are typically based on extracting features manually. Machine learning such as support vector machines (SVMs) random forests commonly used for these domains. Although methods have generated encouraging results, they frequently limited by the handcrafted feature quality sensitivity of machine algorithms hyperparameters. Deep has sparked a revolution in segmentation tasks automating extraction complex from medical images, eliminating need manual engineering. As result, imaging techniques improving terms accuracy robustness. In realm imaging, deep primarily leverage convolutional neural network (CNN) architectures. Notably, models U-Net, Attention ResUNet gained popularity 3D image segmentation. CNNs excel at producing probability maps each pixel input image, indicating likelihood belonging specific class. However, fall short capturing channel-level information. To address this limitation, newer variations squeeze excitation embedded attention U-Net (SEEA-U-Net) emerged, focusing within regions interest analysis. These advancements find extensive application tumor offer adaptability through architectural adjustments, striking fine balance between computational efficiency precision. The combination diverse CNN designs proven be robust strategy adapting various modalities. Inner structure segmentation, including cochlea preoperative CT helps diagnosis treatment hearing impairments, especially cochlear implant surgery. Experiments demonstrated exceptional performance super-resolution architecture known SRSegN (i.e. network). With mean Dice score exceeding 0.80, outperforms several established models. This builds upon encoder–decoder incorporating branch up-sampling that incrementally extracts low-resolution inputs upscales maps. chapter, we elucidate profound impact head anatomies, spanning applications detection, vessel self-learning capabilities inherent ushered transformative era, significantly enhancing effectiveness outcomes critical tasks.
Language: Английский
Citations
0Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 104, P. 107560 - 107560
Published: Jan. 28, 2025
Language: Английский
Citations
0Iranian Journal of Science and Technology Transactions of Electrical Engineering, Journal Year: 2025, Volume and Issue: unknown
Published: March 12, 2025
Language: Английский
Citations
0Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 107, P. 107865 - 107865
Published: March 26, 2025
Language: Английский
Citations
0Discover Artificial Intelligence, Journal Year: 2025, Volume and Issue: 5(1)
Published: March 27, 2025
Language: Английский
Citations
0Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown
Published: April 4, 2025
Language: Английский
Citations
0