AIMC Topic: Brain Neoplasms

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A federated learning-based privacy-preserving image processing framework for brain tumor detection from CT scans.

Scientific reports Jul 2, 2025
The detection of brain tumors is crucial in medical imaging, because accurate and early diagnosis can have a positive effect on patients. Because traditional deep learning models store all their data together, they raise questions about privacy, comp...
Humans Neural Networks, Computer Algorithms Image Processing, Computer-Assisted Privacy Federated Learning Tomography, X-Ray Computed Brain Neoplasms Deep Learning
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Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI.

Scientific reports Jul 2, 2025
Automated segmentation of pediatric brain tumors (PBTs) can support precise diagnosis and treatment monitoring, but it is still poorly investigated in literature. This study proposes two different Deep Learning approaches for semantic segmentation of...
Deep Learning Child, Preschool Child Brain Neoplasms Humans Semantics Female Male Image Processing, Computer-Assisted Multiparametric Magnetic Resonance Imaging Glioma Magnetic Resonance Imaging
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Hierarchical in-out fusion for incomplete multimodal brain tumor segmentation.

Scientific reports Jul 2, 2025
Fusing multimodal data play a crucial role in accurate brain tumor segmentation network and clinical diagnosis, especially in scenarios with incomplete multimodal data. Existing multimodal fusion models usually perform intra-modal fusion at both shal...
Neural Networks, Computer Algorithms Brain Neoplasms Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Multimodal Imaging
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Multi channel fusion diffusion models for brain tumor MRI data augmentation.

Scientific reports Jul 2, 2025
The early diagnosis of brain tumors is crucial for patient prognosis, and medical imaging techniques such as MRI and CT scans are essential tools for diagnosing brain tumors. However, high-quality medical image data for brain tumors is often scarce a...
Brain Neoplasms Deep Learning Image Processing, Computer-Assisted Humans Algorithms Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Diffusion Magnetic Resonance Imaging
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Lightweight convolutional neural networks using nonlinear Lévy chaotic moth flame optimisation for brain tumour classification via efficient hyperparameter tuning.

Scientific reports Jul 2, 2025
Deep convolutional neural networks (CNNs) have seen significant growth in medical image classification applications due to their ability to automate feature extraction, leverage hierarchical learning, and deliver high classification accuracy. However...
Humans Neural Networks, Computer Algorithms Image Processing, Computer-Assisted Brain Neoplasms Nonlinear Dynamics Convolutional Neural Networks
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Hybrid model integration with explainable AI for brain tumor diagnosis: a unified approach to MRI analysis and prediction.

Scientific reports Jul 1, 2025
Effective treatment for brain tumors relies on accurate detection because this is a crucial health condition. Medical imaging plays a pivotal role in improving tumor detection and diagnosis in the early stage. This study presents two approaches to th...
Image Processing, Computer-Assisted Algorithms Humans Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Brain Neoplasms Machine Learning Deep Learning
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Hybrid transfer learning and self-attention framework for robust MRI-based brain tumor classification.

Scientific reports Jul 1, 2025
Brain tumors are a significant contributor to cancer-related deaths worldwide. Accurate and prompt detection is crucial to reduce mortality rates and improve patient survival prospects. Magnetic Resonance Imaging (MRI) is crucial for diagnosis, but m...
Image Interpretation, Computer-Assisted Diagnosis, Computer-Assisted Deep Learning Magnetic Resonance Imaging Neural Networks, Computer Brain Neoplasms Humans
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Synergizing advanced algorithm of explainable artificial intelligence with hybrid model for enhanced brain tumor detection in healthcare.

Scientific reports Jul 1, 2025
Brain tumor causes life-threatening consequences due to which its timely detection and accurate classification are critical for determining appropriate treatment plans while focusing on the improved patient outcomes. However, conventional approaches ...
Humans Deep Learning Artificial Intelligence Tomography, X-Ray Computed Machine Learning Magnetic Resonance Imaging Brain Neoplasms Algorithms
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Generative AI for weakly supervised segmentation and downstream classification of brain tumors on MR images.

Scientific reports Jul 1, 2025
Segmenting abnormalities is a leading problem in medical imaging. Using machine learning for segmentation generally requires manually annotated segmentations, demanding extensive time and resources from radiologists. We propose a weakly supervised ap...
Image Processing, Computer-Assisted Glioma Magnetic Resonance Imaging Supervised Machine Learning Image Interpretation, Computer-Assisted Algorithms Brain Neoplasms Child Humans Machine Learning
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A highly generalized federated learning algorithm for brain tumor segmentation.

Scientific reports Jul 1, 2025
Brain image segmentation plays a pivotal role in modern healthcare by enabling precise diagnosis and treatment planning. Federated Learning (FL) enables collaborative model training across institutions while safeguarding sensitive patient data. The i...
Artificial Intelligence Machine Learning Humans Algorithms Brain Neoplasms Magnetic Resonance Imaging Image Processing, Computer-Assisted Federated Learning
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