SAlexNet: Superimposed AlexNet using Residual Attention Mechanism for Accurate and Efficient Automatic Primary Brain Tumor Detection and Classification

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104025 - 104025

Опубликована: Янв. 1, 2025

Язык: Английский

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ACU-Net: Attention-based convolutional U-Net model for segmenting brain tumors in fMRI images

Digital Health, Год журнала: 2025, Номер 11

Опубликована: Янв. 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.

Язык: Английский

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A multi-stage fusion deep learning framework merging local patterns with attention-driven contextual dependencies for cancer detection

Computers in Biology and Medicine, Год журнала: 2025, Номер 189, С. 109916 - 109916

Опубликована: Март 6, 2025

Язык: Английский

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ViT-CB: Integrating hybrid Vision Transformer and CatBoost to enhanced brain tumor detection with SHAP

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 100, С. 107027 - 107027

Опубликована: Окт. 24, 2024

Язык: Английский

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Deep Learning Approaches for Brain Tumor Detection and Classification Using MRI Images (2020 to 2024): A Systematic Review

Deleted Journal, Год журнала: 2024, Номер unknown

Опубликована: Сен. 30, 2024

Brain tumor is a type of disease caused by uncontrolled cell proliferation in the brain leading to serious health issues such as memory loss and motor impairment. Therefore, early diagnosis tumors plays crucial role extend survival patients. However, given busy nature work radiologists aiming reduce likelihood false diagnoses, advancing technologies including computer-aided artificial intelligence have shown an important assisting radiologists. In recent years, number deep learning-based methods been applied for detection classification using MRI images achieved promising results. The main objective this paper present detailed review previous researches field. addition, This summarizes existing limitations significant highlights. study systematically reviews 60 articles published between 2020 January 2024, extensively covering transfer learning, autoencoders, transformers, attention mechanisms. key findings formulated provide analytic comparison future directions. aims comprehensive understanding automatic techniques that may be useful professionals academic communities working on detection.

Язык: Английский

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Deep Learning Transformations in Medical Imaging: Advancements in Brain Tumor, Retinal Vessel, and Inner Ear Segmentation

Опубликована: Янв. 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.

Язык: Английский

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MRI intracranial Neoplasm classification using hybrid LOA-based deep learning classifier

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 104, С. 107560 - 107560

Опубликована: Янв. 28, 2025

Язык: Английский

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Advanced Brain Tumor MR Image Classification Using a Combination Undecimated Wavelet Transform, EfficientNet-B0 and PCA via Multi-SVM Analysis

Iranian Journal of Science and Technology Transactions of Electrical Engineering, Год журнала: 2025, Номер unknown

Опубликована: Март 12, 2025

Язык: Английский

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EC-HDLNet: Extended coati-based hybrid deep dilated convolutional learning network for brain tumor classification

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 107, С. 107865 - 107865

Опубликована: Март 26, 2025

Язык: Английский

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From black box AI to XAI in neuro-oncology: a survey on MRI-based tumor detection

Discover Artificial Intelligence, Год журнала: 2025, Номер 5(1)

Опубликована: Март 27, 2025

Язык: Английский

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