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retinopathy-detection's Introduction

retinopathy-detection

Objective

In this project, an executable program that diagnoses diseases in the retina and recommends a treatment based on the patient's risk factors has been developed.

Development

From a dataset of 80000 images classified in CNV (Choroidal Neurovascularization), DME (Diabetic Macular Edema), Drusen and Normal, a convolutional neural network has been developed by transferring and adapting the architecture of the trained model InceptionV3.

Thus, by developing a functional model in Keras, an accuracy of 93% has been obtained with a loss value of 0.23.

User Interface

The PyQt5 library has been used to design the graphic interface. Through it, the user can select the image, know its diagnostic, save it in a PDF and discover the treatments that have been done to patients with the same pathology and similar risk factors.

Links

kaggle dataset: https://www.kaggle.com/paultimothymooney/kermany2018

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