Year: 2025
Type: article
Abstract: The accurate and timely classification of brain tumors is critical for effective treatment and patient outcomes.This research proposes a hybrid deep learning model to automatically classify brain cancers from medical photos by utilizing both Transformers and Convolutional Neural Networks (CNNs).The Transformer architecture improves the model's comprehension of global linkages and contextual dependencies between these data, while the CNN is used as a feature extractor to extract complex spatial features from MRI scans.This combination efficiently uses both local and global imaging information to enable more accurate tumor type categorization.Using a dataset of 2270 brain MRI images, the model is trained and assessed, and it performs well on important metrics like accuracy, precision, recall, and F1-score.The suggested system shows great promise for increasing diagnostic efficiency and accuracy, providing doctors with a dependable tool to support early tumor diagnosis and individualized treatment planning.Expanding the dataset, improving the architecture of the model, and using the system in clinical settings for real-time tumor classification are possible future.
Authors: Mr. D. Gunna Manoj, Chiguru harika, Vadde Jashnavi
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: bronze
Funder: Strong
OpenAlex ID: W4410339178