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In this project, we've used machine learning models to diagnose retinal and optic nerve diseases, including Choroidal Neovascularization, Diabetic Macular Edema, and Drusen, using the OCT-MNIST dataset of 109,309 images. We created two models: a logistic regression + Random Forest pipeline and a CNN. The CNN model showed superior performance.

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NiloofarT/OCT_Classification

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OCT_Classification

In this project, we used machine learning to diagnose retinal and optic nerve diseases, including Choroidal Neovascularization, Diabetic Macular Edema, and Drusen, using the OCT-MNIST dataset of 109,309 images. We created two models: a logistic regression + Random Forest pipeline and a CNN. The CNN model showed superior performance.

Data

The dataset used in this project can be obtained from the original source: https://doi.org/10.1016/j.cell.2018.02.010

Please follow the terms and conditions specified by the authors before downloading and using the dataset.

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In this project, we've used machine learning models to diagnose retinal and optic nerve diseases, including Choroidal Neovascularization, Diabetic Macular Edema, and Drusen, using the OCT-MNIST dataset of 109,309 images. We created two models: a logistic regression + Random Forest pipeline and a CNN. The CNN model showed superior performance.

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