Advancing Brain Tumor Diagnosis: The Role of Convolutional Neural Networks
Year: 2025
Type: article
Abstract: Brain tumor classification plays a vital role in early diagnosis and treatment planning, directly impacting patient survival. Traditional MRI-based diagnosis relies on radiologists, making it subjective and time-consuming. Deep learning, particularly Convolutional Neural Networks (CNNs), has revolutionized this process by automatically identifying tumors with high accuracy. CNNs analyze MRI scans to distinguish tumor types like gliomas, meningiomas, and pituitary adenomas, achieving accuracies above 98%. Techniques like transfer learning and data augmentation enhance performance, even with limited data. However, challenges remain—dataset limitations, model interpretability, and high computational costs slow adoption. Future efforts must focus on clinical validation, ethical AI deployment, and federated learning for privacy. With these improvements, AI-driven diagnostics can support radiologists, ensuring faster and more reliable brain tumor detection.
Authors: P. Deepthi Chandan, Syeda Hifsa Naaz
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W6893183635
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