Brain-Tumor-Detection Open-Source Brain Tumor Detection Model - High Accuracy Empowers Image Classification and Diagnosis

Brain Tumor Detection

Developed by ShimaGh

A brain tumor detection model based on the Swin Transformer architecture, achieving 98.04% accuracy in image classification tasks

Image Classification

Transformers

Open Source License:Apache-2.0 #Medical Image Classification #High-Precision Detection #Swin-Transformer

Downloads 421

Release Time : 2/13/2024

Model Overview

This model is a fine-tuned brain tumor detection model based on microsoft/swin-base-patch4-window7-224, specifically designed for medical image classification tasks, capable of identifying tumor lesions in brain scan images

Model Features

High Accuracy

Achieves 98.04% classification accuracy on the evaluation set

Based on Swin Transformer

Utilizes the advanced Swin Transformer architecture, suitable for processing medical images

Lightweight Fine-Tuning

Efficient fine-tuning on the base model while retaining the knowledge of the pre-trained model

Model Capabilities

Medical Image Classification

Brain Tumor Detection

Image Feature Extraction

Use Cases

Medical Diagnosis

Brain Tumor Assisted Diagnosis

Assists radiologists in identifying tumor lesions in brain scan images

98.04% accuracy

Medical Image Analysis

Automatically analyzes MRI or CT scan images

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.92	3	0.4395	0.6667
No log	1.85	6	0.2817	0.9020
No log	2.77	9	0.1354	0.9608
0.3994	4.0	13	0.0956	0.9804
0.3994	4.62	15	0.0946	0.9804

Property	Details
Model Type	Fine - tuned version of microsoft/swin - base - patch4 - window7 - 224
Training Data	imagefolder
Metrics	accuracy

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご

Brain Tumor Detection

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 Brain-Tumor-Detection

🚀 Quick Start

✨ Features

📚 Documentation

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

📄 License