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Vit Base Patch16 224 In21k Finetuned Brain Tumor
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Vit Base Patch16 224 In21k Finetuned Brain Tumor

Developed by amjadfqs
This model is a fine-tuned version of Google's ViT base model on a brain tumor image dataset, designed for brain tumor image classification tasks, achieving a test set accuracy of 93.16%.
Image Classification
Transformers
Open Source License:Apache-2.0#Brain Tumor Classification#High-precision Medical Imaging#Fine-tuned ViT Model
Downloads 41
Release Time : 2/16/2023

Model Overview

An image classification model based on the Vision Transformer architecture, specifically fine-tuned for brain tumor recognition tasks, suitable for the field of medical image analysis.

Model Features

High-precision Classification
Achieves a classification accuracy of 93.16% on the brain tumor test set.
Fine-tuned Pre-trained Model
Domain adaptation based on a large-scale pre-trained ViT model.
Optimized for Medical Imaging
Specifically optimized for brain tumor recognition tasks.

Model Capabilities

Medical Image Classification
Brain Tumor Recognition
Image Feature Extraction

Use Cases

Medical Diagnosis
Brain Tumor Auxiliary Diagnosis
Automatically identifies brain tumor types through MRI images.
Test set accuracy of 93.16%.
Medical Research
Medical Image Analysis
Used for image classification tasks in brain tumor-related research.
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