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Brain Tumor Classification Using Swin Transformer
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Brain Tumor Classification Using Swin Transformer

Developed by surajjoshi
This model is a brain tumor image classification model based on the Swin Transformer architecture, achieving outstanding performance in image classification tasks with an accuracy rate of 99.49%.
Image Classification
Transformers
Open Source License:Apache-2.0#Medical Image Classification#Swin Transformer#High-Precision Classification
Downloads 103
Release Time : 1/25/2023

Model Overview

This is a fine-tuned Swin Transformer model specifically designed for brain tumor image classification tasks. The model demonstrates near-perfect classification performance on the evaluation set.

Model Features

High-Precision Classification
Achieves 99.49% accuracy, F1 score, recall, and precision in brain tumor classification tasks.
Based on Swin Transformer
Utilizes the advanced Swin Transformer architecture, well-suited for visual tasks.
Efficient Fine-Tuning
Requires minimal training on the pre-trained model to achieve excellent performance.

Model Capabilities

Medical Image Classification
Brain Tumor Recognition
Image Feature Extraction

Use Cases

Medical Diagnosis
Brain Tumor Screening
Assists doctors in identifying brain tumor types.
Classification accuracy of 99.49%
Medical Image Analysis
Automatically analyzes abnormalities in medical images such as MRI.
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