Skip to content
Log in
Sponsored Artificial Intelligence: A Modern Approach, Global Edition Artificial Intelligence: A Modern Approach, Global Edition
Open access

A Multimodal Deep Learning Framework for Brain Tumor Classification Using EfficientNet and Vision Transformers

Lovenish Sharma S. Nanda
Aug 2026 · International journal of computer information systems and industrial management applications · Vol 18, pp. 1130-1142 · 0 citations

TL;DR

The proposed model such as MM-EffiFormer demonstrated the significant classification performance by achieving Accuracy of 0.990, Sensitivity of 0.987, Specificity of 0.995, Dice Similarity Coefficient (DSC) of 0.991, outperforms the existing model FCM-SVM.

Abstract

Brain tumors are becoming life-threatening issues when not treated at earlier stages. Accurate brain tumor detection is essential in order to support clinical decisions and diagnosis. The study uses the multimodal deep learning framework for brain tumor classification by effectively combining the EfficientNet-B7 network and vision transformer architectures. Modality specific feature extraction has been carried out using the EfficientNet-B7 network for both the Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Then feature maps are converted into token sequence and passed through the transformer encoder layers in order to effectively model the global dependencies across the image. Then these features are fused and then classified using the fully connected layers whether it is healthy or tumor. Based on the experimental evaluation, the proposed model in this study such as MM-EffiFormer demonstrated the significant classification performance by achieving Accuracy of 0.990, Sensitivity of 0.987, Specificity of 0.995, Dice Similarity Coefficient (DSC) of 0.991, outperforms the existing model FCM-SVM.

DOI Semantic Scholar
Read PDF

Similar papers

Open access 2026

Accurate Brain Tumor Classification Using MRI Images Based on A Hybrid Vision Transformer and BiLSTM Framework

Results show that ViT–BiLSTM's classification performance is superior to those of traditional deep learning methods: among all the tumor categories its accuracy is higher, its fine-tuning more perfect, as well as, its Recall rates greater.

Nagham Salim Mohammed, Omar S. Almolaa, A. S. Abdullah et al. · 0 citations
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 tumor regions, thereby improving transparency and trustworthiness.

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

Enhanced Brain Tumor Detection and Classification Using Transfer Learning and Explainable AI with Clinical Report Generator

Correctly sorting brain tumors captured through Magnetic Resonance Imaging (MRI) plays a vital role in timely diagnosis and sound clinical decision-making, since a delayed or wrong call can seriously harm patient outcomes. This work introduces AMC-NeuroDx, an upgraded deep-learning pipeline that pits a Custom Convolutional Neural Network (CNN) built from the ground up against a ResNet50 transfer-learning model on a four-way brain tumor classification task (Glioma, Meningioma, Pituitary Tumor, and No Tumor), drawing on 7,223 MRI scans taken from the Kaggle Brain Tumor MRI dataset. ResNet50 follows a two-stage routine—training with a frozen base before full fine-tuning—and reaches 93.16% validation accuracy after only 20 epochs, whereas the Custom CNN needs 50 epochs to hit 90.70%. The framework also embeds Grad-CAM so that spatial heatmaps can show which brain regions drove each prediction, meeting the clinical demand for transparency. On top of this, a Streamlit interface lets users upload MRI scans in real time and automatically produces patient-specific PDF clinical reports holding the diagnosis, confidence scores, Grad-CAM overlays, and recommended next steps. The results obtained here show that transfer learning yields higher accuracy and quicker convergence than building a CNN from the ground up, and together with the built-in explainability and automated report writing, this makes AMC-NeuroDx a practical candidate for real clinical settings.

K. R, M. M · 0 citations
Open access Jul 2026

Comparing Explainable Artificial Intelligence and Deep Learning Models for MRI-Based Brain Tumor Diagnosis

This study investigates the application of deep learning architectures, including Convolutional Neural Network, VGG16, VGG19, ResNet50, and MobileNet, for brain tumor detection and classification and confirms that advanced deep learning architectures not only achieve high classification accuracy but also improve interpretability, thereby offering reliable and clinically applicable solutions for automated brain tumor diagnosis.

H. Uzel, Feyyaz Alpsalaz, Yıldırım Özüpak et al. · 0 citations
Open access Jul 2026

Development of Brain Tumor Classification System using Convolutional Neural Network Model with Explainable Artificial Intelligence

A brain tumor classification system integrated with Explainable Artificial Intelligence (XAI) was developed using MRI images and demonstrated effective classification performance and improved interpretability, making it suitable for automated brain tumor diagnosis.

T. H. Stephen, A. Oke, A. S. Falohun et al. · 0 citations

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