Meningioma, the most prevalent primary intracranial tumor, presents significant clinical challenges due to unclear molecular mechanisms underlying its progression from low-grade (LG) to high-grade (HG) and lack of grade-specific biomarkers. Here, we ...
This study aimed to develop a novel diagnostic tool for detecting meningioma using skull X-ray images, combining deep learning with traditional machine learning classifiers. The goal was to explore the potential of using a cost-effective and widely a...
Automated brain tumor detection represents a fundamental challenge in contemporary medical imaging, demanding both precision and computational feasibility for practical implementation. This research introduces a novel Vision Transformer (ViT) framewo...
Brain tumors are among the most fatal diseases, Often leading to a reduction in life expectancy. Early and accurate diagnosis is essential to guide effective treatment and enhance survival rates. Advances in artificial intelligence, particularly deep...
The increasing prevalence of brain tumors calls for the development of accurate and reliable diagnostic tools. Whereas traditional techniques offer some benefits, they can hardly detect or accurately classify the type of a tumor at an early stage, cr...
The uncontrollable and rapid growth of brain cells can lead to brain tumors. If left untreated, this condition may result in severe health consequences, including death. Accurate detection and classification are the essential steps toward understandi...
Meningiomas and schwannomas are benign tumors that affect the central nervous system, comprising up to one-third of intracranial neoplasms. Gamma Knife radiosurgery (GKRS), or stereotactic radiosurgery (SRS), is a form of radiation therapy. Although ...
BACKGROUND: Predicting recurrence in meningioma patients is vital for improving long-term outcomes and tailoring personalized treatment strategies. While traditional diagnostic methods have advanced, accurately forecasting recurrence remains a persis...
OBJECTIVE: This study aims to develop a robust and clinically applicable framework for preoperative grading of meningiomas using T1-contrast-enhanced and T2-weighted MRI images. The approach integrates radiomic feature extraction, attention-guided de...
Early and accurate brain tumor classification is vital for clinical diagnosis and treatment. Although Convolutional Neural Networks (CNNs) are widely used in medical image analysis, they often struggle to focus on critical information adequately and ...
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