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Diabetic Retinopathy Detection

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# Diabetic Retinopathy Detection

**Problem Statement**

Diabetic Retinopathy (DR) is a diabetes-related eye disease that can lead to permanent vision loss if not detected early. Traditional screening methods require manual examination by medical professionals, which is time-consuming and prone to variability due to human interpretation.

The goal of this project is to develop an automated system that can accurately classify the severity of Diabetic Retinopathy from retinal fundus images. This helps support early diagnosis and may assist healthcare providers in managing patient screening more efficiently.

**Dataset = APTOS 2019 Blindness Detection**
# Solution
To address this problem, a pretrained EfficientNet-B6 model was utilized for classifying the severity levels of Diabetic Retinopathy. The model was fine-tuned on labeled retinal images to recognize different stages of DR

Key features of the solution:

Transfer learning using EfficientNet-B6

Multi-class classification for DR severity

Preprocessing pipeline for image cleaning and normalization
**QWK = 0.8940**
# Preprocessing
![Jse3eq3A](https://github.com/user-attachments/assets/a35bd3bc-192c-4f24-a873-9847c9f97ca5)

# Application Interface Demo

![diabetic_classifier](https://github.com/user-attachments/assets/4d5ecf1c-3711-4fdf-99e5-33f432c0476a)

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