Enhancing Brain Tumor Diagnosis through Deep Feature Learning and Spatial Context Awareness in MRI Scans

Abstract

Brain tumor classification is a critical task in medical diagnostics, where accurate and timely detection significantly influences treatment planning and patient outcomes. In this study, a hybrid deep learning framework which joins Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) is developed to classify brain tumors based on the BRATS 2021 dataset using the FLAIR modality. The CNN part learns local patterns (e.g. edges and textures) while the ViT part captures global dependencies over the whole images, which provides insights of the tumor regions in a holistic way. After performing pre-processing on the dataset, which comprised of 53,610 images, the images were partitioned into tumor and non-tumor groups. The training, validation, and test sets were created with 80%, 10%, and 10%, respectively. The listed model performed two independently trained models for 100 epochs at first, then is applied a feature fusion technique which exploits the complementarity of both architectures. The hybrid model was assessed based on conventional metrics like precision, recall, F1-score, and accuracy. The result confirmed that the hybrid method achieved better performance than the individual CNN and ViT models, with improvements in precision and strengthened classification capability. The study emphasizes how hybrid deep learning techniques can improve clinical applications by alleviating challenges related to data variability, noise, and image complexity. The proposed method combining local and global feature extraction methods ensures valuable typing providing a reliable and scalable implementation of automatic brain tumor classification helping improve the accuracy and efficiency of healthcare related problem solutions and medical diagnostics.