Enhancing Brain Tumor Classification Using Vision Transformers with Colormap-Based Feature Representation on BRISC2025 Dataset

• URL: http://arxiv.org/abs/2603.21234v1
• Date: Sun, 22 Mar 2026 13:46:05 GMT
• Title: Enhancing Brain Tumor Classification Using Vision Transformers with Colormap-Based Feature Representation on BRISC2025 Dataset
• Abstract summary: We propose a deep learning framework based on Vision Transformers (ViT) enhanced with colormap-based feature representation to improve brain tumor classification performance.<n>Experiments are conducted on the BRISC2025 dataset, which includes four classes: glioma, meningioma, pituitary tumor, and non-tumor cases.<n>The proposed method achieves a classification accuracy of 98.90%, outperforming baseline convolutional neural network models.
• Score: 0.8374077003751697
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate classification of brain tumors from magnetic resonance imaging (MRI) plays a critical role in early diagnosis and effective treatment planning. In this study, we propose a deep learning framework based on Vision Transformers (ViT) enhanced with colormap-based feature representation to improve multi-class brain tumor classification performance. The proposed approach leverages the ability of transformer architectures to capture long-range dependencies while incorporating color mapping techniques to emphasize important structural and intensity variations within MRI scans. Experiments are conducted on the BRISC2025 dataset, which includes four classes: glioma, meningioma, pituitary tumor, and non-tumor cases. The model is trained and evaluated using standard performance metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The proposed method achieves a classification accuracy of 98.90%, outperforming baseline convolutional neural network models including ResNet50, ResNet101, and EfficientNetB2. In addition, the model demonstrates strong generalization capability with an AUC of 99.97%, indicating high discriminative performance across all classes. These results highlight the effectiveness of combining Vision Transformers with colormap-based feature enhancement for accurate and robust brain tumor classification and suggest strong potential for clinical decision support applications.

Related papers

• Bridging Topology and Deep Representation Learning: A TDA-ViT Fusion Model for Four-Class Brain Tumor Classification [0.8374077003751697]
  We propose a fusion framework that combines Topological Data Analysis features with pretrained Vision Transformer representations for four-class brain tumor classification.<n>The proposed TDA-ViT fusion model achieves an accuracy of 99.10%, precision of 99.27%, recall of 99.15%, F1-score of 99.21%, and an AUC of 99.98%.
  arXiv  Detail & Related papers  (2026-05-30T23:28:15Z)
• an interpretable vision transformer framework for automated brain tumor classification [0.04999814847776097]
  This paper proposes a deep learning framework for automated four-class brain tumor classification.<n>The proposed system employs a Vision Transformer (ViT-B/16) pretrained on ImageNet-21k as the backbone.<n>The proposed model achieves a test accuracy of 99.29%, macro F1-score of 99.25%, and perfect recall on both healthy and meningioma classes.
  arXiv  Detail & Related papers  (2026-04-23T06:07:10Z)
• Optimized Weighted Voting System for Brain Tumor Classification Using MRI Images [0.0]
  This paper presents a weighted ensemble learning approach that combines deep learning and traditional machine learning models to improve classification performance.<n>A weighted voting mechanism assigns higher influence to models with better individual accuracy, ensuring robust decision-making.<n> Experimental evaluations on the Figshare and Kaggle MRI datasets demonstrate that the proposed method achieves state-of-the-art accuracy.
  arXiv  Detail & Related papers  (2026-03-30T12:24:54Z)
• PathMoE: Interpretable Multimodal Interaction Experts for Pediatric Brain Tumor Classification [30.58342408480846]
  PathMoE is an interpretable multimodal framework that integrates H&E slides, pathology reports, and nuclei-level cell graphs.<n>We evaluate our framework on two dataset-specific classification tasks on an internal pediatric brain tumor dataset and external TCGA datasets.
  arXiv  Detail & Related papers  (2026-03-02T07:17:44Z)
• Squeezed-Eff-Net: Edge-Computed Boost of Tomography Based Brain Tumor Classification leveraging Hybrid Neural Network Architecture [0.7829352305480285]
  This work proposes a hybrid deep learning model based on SqueezeNet v1 which is a lightweight model, and EfficientNet-B0, which is a high-performing model.<n>The framework was trained and tested only on publicly available Nickparvar Brain Tumor MRI dataset.
  arXiv  Detail & Related papers  (2025-12-08T07:37:30Z)
• MRI-Based Brain Tumor Detection through an Explainable EfficientNetV2 and MLP-Mixer-Attention Architecture [0.0]
  Brain tumors are serious health problems that require early diagnosis due to their high mortality rates.<n>The need for automated diagnosis systems is increasing day by day.<n>A robust and explainable Deep Learning model for the classification of brain tumors is proposed.
  arXiv  Detail & Related papers  (2025-09-08T14:08:21Z)
• Light Weight CNN for classification of Brain Tumors from MRI Images [0.0]
  This study presents a convolutional neural network (CNN)-based approach for the multi-class classification of brain tumors.<n>We utilize a publicly available dataset containing MRI images categorized into four classes: glioma, meningioma, pituitary tumor, and no tumor.<n> Experimental results demonstrate that the proposed model achieves a classification accuracy of 98.78%, indicating its potential as a diagnostic aid in clinical settings.
  arXiv  Detail & Related papers  (2025-04-29T21:45:11Z)
• Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches [0.0]
  Brain tumors pose a serious health threat due to their rapid growth and potential for metastasis. This study aims to improve the efficiency and accuracy of brain tumor classification. Our approach combines state-of-the-art deep learning algorithms, including the Vision Transformer (ViT), Capsule Neural Network (CapsNet), and convolutional neural networks (CNNs) such as ResNet-152 and VGG16.
  arXiv  Detail & Related papers  (2024-10-31T07:28:06Z)
• Virchow: A Million-Slide Digital Pathology Foundation Model [34.38679208931425]
  We present Virchow, a foundation model for computational pathology. Virchow is a vision transformer model with 632 million parameters trained on 1.5 million hematoxylin and eosin stained whole slide images.
  arXiv  Detail & Related papers  (2023-09-14T15:09:35Z)
• Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network [63.845552349914186]
  Capturing global contextual information plays a critical role in breast ultrasound (BUS) image classification. Vision Transformers have an improved capability of capturing global contextual information but may distort the local image patterns due to the tokenization operations. In this study, we proposed a hybrid multitask deep neural network called Hybrid-MT-ESTAN, designed to perform BUS tumor classification and segmentation.
  arXiv  Detail & Related papers  (2023-08-04T01:19:32Z)
• Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation [28.650980942429726]
  We propose a volumetric vision transformer that follows two windowing strategies in attention for extracting fine features. We trained and evaluated network architecture on the FeTS Challenge 2022 dataset. Our performance on the online validation dataset is as follows: Dice Similarity Score of 81.71%, 91.38% and 85.40%.
  arXiv  Detail & Related papers  (2022-09-16T03:55:48Z)
• CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation [37.39921484146194]
  Brain tumor segmentation in magnetic resonance image (MRI) is crucial for brain tumor diagnosis, cancer management and research purposes. With the great success of the ten-year BraTS challenges, a lot of outstanding BTS models have been proposed to tackle the difficulties of BTS in different technical aspects. We propose a clinical knowledge-driven brain tumor segmentation model, called CKD-TransBTS.
  arXiv  Detail & Related papers  (2022-07-15T09:35:29Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathology Image Classification [30.497184157710873]
  This paper proposes a multi-scale visual transformer model (GasHis-Transformer) for a gastric histopathology image classification (GHIC) task. GasHis-Transformer model is built on two fundamental modules, including a global information module (GIM) and a local information module (LIM)
  arXiv  Detail & Related papers  (2021-04-29T17:46:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.