mr7495/OCT-classification
ROCT-Net: A new ensemble deep convolutional model with improved spatial resolution learning for detecting common diseases from retinal OCT images
This project offers a computer-aided diagnosis (CAD) system that helps ophthalmologists quickly and accurately detect common retinal diseases from Optical Coherence Tomography (OCT) images. You input an OCT image, and the system outputs a classification indicating if the retina is normal or if it shows signs of diseases like AMD, CSR, or Diabetic Retinopathy. This tool is designed for ophthalmologists and eye care professionals seeking to enhance their diagnostic workflow.
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Use this if you are an ophthalmologist looking for an automated system to improve the early and accurate detection of multiple common retinal diseases from OCT images.
Not ideal if you are looking for a diagnostic tool for retinal conditions not covered by the six specific diseases or normal cases included in this model.
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12
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Language
Jupyter Notebook
License
MIT
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Last pushed
Mar 03, 2022
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