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Brain Tumor Classification

Developed by Devarshi
A fine-tuned brain tumor image classification model based on Swin Transformer architecture, achieving 96.47% accuracy on the evaluation set
Image Classification
Transformers
Open Source License:Apache-2.0#Medical Image Classification#High-precision Swin Model#Brain Tumor Diagnosis
Downloads 205
Release Time : 10/31/2022

Model Overview

This model is a brain tumor image classifier fine-tuned from microsoft/swin-tiny-patch4-window7-224, specifically designed to identify brain tumor types from medical images.

Model Features

High-precision Classification
Achieves 96.47% accuracy on the test set with balanced performance in F1 score, recall, and precision
Swin Transformer Architecture
Based on advanced vision Transformer architecture with excellent feature extraction capabilities
Medical Image Optimization
Specially fine-tuned and optimized for brain tumor medical imaging data

Model Capabilities

Brain tumor image classification
Medical image analysis
Multi-category image recognition

Use Cases

Medical Diagnosis
Brain Tumor Auxiliary Diagnosis
Assists radiologists in quickly identifying and classifying brain tumor images
96.47% accuracy
Medical Imaging Research
Serves as a benchmark model in medical image analysis research
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