DR-DDPM
DR-DDPM
Datasets
DR-DDPM
File
DR-DDPM
Use Case
DR-DDPM
Description
Innovative diabetic retinopathy detection dataset combining real and DDPM-generated synthetic images, addressing class imbalance and improving detection accuracy by 9%. Ideal for medical image classification and deep learning research in healthcare.
Description:
This innovative dataset is designed to address the challenges of applying deep learning to medical image classification, particularly for diabetic retinopathy detection. It combines real medical images with synthetic images generated using Denoising Diffusion Probabilistic Models (DDPM). Key features of this dataset include augmentation for diabetic retinopathy detection, a combination of real and DDPM-generated synthetic images, and a standardized image size of 192×192 pixels. The dataset demonstrates an average 9% improvement in detection accuracy and effectively addresses class imbalance issues common in medical datasets.
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This dataset is invaluable for researchers and practitioners focusing on medical image classification, generative models in healthcare, data augmentation techniques, and class imbalance in machine learning. It is especially beneficial for diabetic retinopathy detection and other ophthalmological applications. By making this dataset publicly available, we aim to advance deep learning applications in medical imaging and promote further research in this critical area.
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