Employing CNN Deep Learning for the Detection and Classification of Multiple Types of Brain Tumors
Year: 2025
Type: article
Abstract: Brain tumors, a leading cause of mortality, necessitate accurate and timely detection for effective treatment. Recent advancements in deep learning and medical imaging have significantly enhanced the capability to identify and classify cancers. This extensive study focuses on three specific tumor types: glioma, meningioma, and pituitary tumors and explores the application of deep transfer learning methods for brain tumor classification utilizing convolutional neural networks (CNN). By analyzing MRI scans, the research assesses the performance of pretrained CNN models, including ResNet-50, Inception-v3, and VGG-16 in automating tumor prediction. The study employs performance metrics such as accuracy, precision, recall, and F1 scores to evaluate how effectively each model aids in early diagnosis and improves clinical decision-making.
Authors: Sugan M, Helen Sunomitha Nancy
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W4409306536