Skip to content
Log in
Sponsored AI AM CLAUDE, A Beginner's Guide: Learn the basics of working with AI AI AM CLAUDE, A Beginner's Guide: Learn the basics of working with AI

A hybrid Wavelet–EfficientNetV2–Vision transformer framework for multiclass brain tumor MRI classification

Oussama Abda Hilal Naimi Mohammed Bourennane Shoffan Saifullah
Aug 2026 · Physical and Engineering Sciences in Medicine · Vol 49, pp. 1867 - 1891 · 0 citations · 50 references
Medicine

TL;DR

A hybrid Wavelet–EfficientNetV2–Vision Transformer (ViT) framework that integrates multi-resolution frequency enhancement, hierarchical spatial encoding, and global dependency modeling into a unified representation offers a promising direction for developing reliable and high-performance computer-aided diagnosis systems.

DOI PubMed Semantic Scholar
View source

Similar papers

Open access Aug 2026

XAIViT: An Explainable Vision Transformer Framework for Multi-Class Brain Tumor Classification in MRI Images

The proposed XAIViT framework has strong potential as an Explainable Artificial Intelligence (XAI)-based clinical decision support system for MRI-based brain tumor analysis and Gradient-weighted Class Activation Mapping-based visual explanations demonstrated that the model consistently focused on anatomically relevant...

I. H. A. Wahab, M. Jamil, Rosihan Rosihan · 0 citations
Open access Sep 2026

A Hybrid Gabor–ViT Embedding with Gated Mamba Deep Learning Framework for Brain Tumor MRI Classification

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...

Baidaa Mutasher Rashed, Zahoor M. Aydam, N. El Abbadi · 0 citations
Open access Aug 2026

Hybrid CNN-Transformer Framework for Multi-Disease Detection from Medical Imaging Data

The proposed framework is intended to support clinical image assessment and prioritization rather than replace expert diagnosis, and demonstrates the potential of hybrid CNN-Transformer architectures for robust and scalable computer-assisted multi-disease screening from medical imaging data.

M. Balakrishnan, K. Ananthi, S. R. et al. · 0 citations
Open access Aug 2026

Improving Brain Tumor Classification In Mri Using Hybrid Mobilenetv2-Densenet121 With Cbam

Brain tumor classification from magnetic resonance imaging (MRI) images requires robust feature learning because tumor classes show subtle visual differences, especially between glioma and meningioma. This work improves accuracy and stability in multi-class brain tumor classification by enriching features and directing...

Jamilatul Muyasaroh, W. Wiharto, Endra Pratama · 0 citations
Open access 2026

Hybrid U-Net++–Vision Transformer Fusion for Accurate Brain MRI Image Segmentation and classifications

The combined segmentation and classification results indicate that the proposed framework provides an effective approach for tumor localization and tumor-type identification, with potential applicability in automated brain MRI analysis and clinical decision support.

Malathi Janapati, Shaheda Akthar · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.