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Vit Base Patch16 224 In21k GI Diagnosis

Developed by DunnBC22
A gastrointestinal image classification model based on ViT architecture for diagnosing various conditions from colonoscopy images
Downloads 22
Release Time : 1/6/2023

Model Overview

This is a gastrointestinal diagnostic image classification model based on the Vision Transformer (ViT) architecture, capable of identifying multiple types of conditions from colonoscopy images.

Model Features

High Accuracy
Achieves 93.75% accuracy on the evaluation set
Multi-metric Optimization
Performs well-balanced across multiple metrics including F1 score, recall, and precision
ViT-based Architecture
Uses Vision Transformer architecture, suitable for processing medical images

Model Capabilities

Gastrointestinal Image Classification
Medical Image Analysis
Multi-class Diagnosis

Use Cases

Medical Diagnosis
Colonoscopy-assisted Diagnosis
Assists doctors in identifying potential conditions from colonoscopy images
93.75% accuracy
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