TL;DR
Findings indicate that colormap-guided feature enhancement can effectively complement Vision Transformers for robust brain tumor classification and may provide a practical direction for improving transformer-based medical image analysis systems.
Accurate brain tumor classification from magnetic resonance imaging (MRI) remains difficult because glioma, meningioma, and pituitary tumors may exhibit overlapping enhancement, morphology, and slice-dependent appearance. This study presents a controlled comparison of Vision Transformer, Data-efficient Image Transforme...
The findings demonstrate the potential of combining complementary transfer-learning models with preprocessing, ensemble fusion, and explainable artificial intelligence for automated brain tumor classification from MRI images.
A two-stage deep learning pipeline for brain tumor classification from MRI images is proposed that integrates normal/abnormal screening, detailed classification, visual explanation, and web-based access within a single two-stage pipeline.
The proposed explainable deep learning framework shows great promise of helping clinical diagnosis of brain tumors to be reliable and transparent, by integrating with AI.
This study introduces a new hybrid deep learning method that integrates Gabor filtering for the improvement in texture feature extraction, the Vision Transformer framework for extraction of the global context dependency and fine-grained spatial features, and a novel Gated Mamba to enhance feature classification efficie...
The findings show that the proposed framework can offer reliable, precise, and interpretable tumor detection and is thus a promising tool in the field of computer-assisted diagnosis and clinical decision support used in neuroimaging studies.
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