眼疾病数据集
该数据集是一个包含眼部疾病医学图像的公开数据集，数据模态为医学影像，主要用于通过深度学习技术（如迁移学习和数据增强）进行眼疾病的自动分类与识别，支持眼科人工智能和计算机辅助诊断的研究与应用。
基本信息
资源简介
眼疾病数据集
【数据集背景】
本数据集源站标题为《cagopat/Transfer-Learning-For-Eye-Diseases-Dataset》。源站首次发布：2024-08-23。
【数据内容】
源站原始描述：# Transfer-Learning-For-Eye-Diseases-Dataset # Overview This project focuses on classifying various eye diseases using deep learning techniques, particularly transfer learning and data augmentation. The model leverages pre-trained convolutional neural networks (CNNs) and fine-tunes them to accurately classify eye diseases from medical images. The dataset used for this project can be found on Kaggle: Eye Diseases Dataset. # Features Transfer Learning: VGG19 is the base model as it displayed a str…
【获取与许可】
源站页面：https://github.com/cagopat/Transfer-Learning-For-Eye-Diseases-Dataset
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取
git clone https://github.com/cagopat/Transfer-Learning-For-Eye-Diseases-Dataset.git
curl -L -o repo.zip https://github.com/cagopat/Transfer-Learning-For-Eye-Diseases-Dataset/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
Transfer-Learning-For-Eye-Diseases-Dataset
Overview
This project focuses on classifying various eye diseases using deep learning techniques, particularly transfer learning and data augmentation. The model leverages pre-trained convolutional neural networks (CNNs) and fine-tunes them to accurately classify eye diseases from medical images.
The dataset used for this project can be found on Kaggle: Eye Diseases Dataset.
Features
Transfer Learning: VGG19 is the base model as it displayed a stronger performance on this particular dataset than the other architectures like Xception.
Data Augmentation: Applies techniques such as rotation, flipping, and zooming to artificially increase the size of the training dataset and improve model generalization.
Evaluation: The model’'s performance is evaluated using metrics like accuracy, precision, recall, and F1-score.
Results
The project achieved an accuracy of 96.68% on the test set, demonstrating the effectiveness of transfer learning and data augmentation in improving model performance on limited data.
Confusion Matrix
Acknowledgments
The project uses pre-trained models from Keras Applications.
Data augmentation techniques inspired by various deep learning research papers.
数据加载示例（图像类）
from PIL import Image
import glob, os
files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)
# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path) # 要求 子目录=类别
目录组织与标注格式以源站说明和下载后实际文件为准。
完整仓库：github.com/cagopat/Transfer-Learning-For-Eye-Diseases-Dataset
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
⚡️ 需要数据支持或标注服务？立即联系我们获取专业方案。
