May 2020: Completed #educational Weight: 10.0

# DIBRD

Hidden

Predict Diabetic Retinopathy

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🛠 Contribute: Found a typo? Or any other change in the description that you would like to see? Please consider sending us a pull request in the public repo of the challenge here.

## 🕵️ Introduction

Diabetic Retinopathy is the leading cause of blindness in the working-age population of the developed world. Deep Learning has given us tremendous power in the field of computer vision. In some fields of vision ,computers can now see and perceive beyond human capabilities. But with great power comes responsibility. The problem we have for you is to `classify` the patient's `retina` as being `diabetic` or `not diabetic` taking into consideration the available image features in the dataset. To know more about diabetic retinopathy click here.

Understand with code! Here is getting started code for you.😄

## 💾 Dataset

This dataset contains features extracted from the Messidor image set to predict whether an image contains signs of diabetic retinopathy or not. There are total of 20 attributes to this dataset, out of which first `19` attributes represents a `descriptive features` extracted from the image set. Last attribute label is `1` if image shows signs of `Diabetic Retinopathy` and `0` if image does `not` show signs of Diabetic Retinopathy. For details about attributes visit here!.

## 📁 Files

Following files can be found in `resources` section:

• `train.csv` - (`920` samples) File that should be used for training. It contains in csv format, the feature representation of the images along with the binary label for each such representation.
• `test.csv` - (`230` samples) File that will be used for actual evaluation for the leaderboard score. It contains only the feature representation of the images and not their binary labels.

## 🚀 Submission

• Prepare a csv containing header as label and predicted value as digit `0` or `1` representing whether or not the image shows signs of `diabetic retionpathy`.
• The name of above file should be `submission.csv`.
• Sample submission format available at `sample_submission.csv`.

Make your first submission here 🚀 !!

## 🖊 Evaluation Criteria

During evaluation F1 score will be used to test the efficiency of the model where,

$F1 = 2 * \frac{precision*recall}{precision+recall}$

## 📚 References

• Dr. Balint Antal, Department of Computer Graphics and Image Processing Faculty of Informatics, University of Debrecen, 4010, Debrecen, POB 12, Hungary

• Dr. Andras Hajdu, Department of Computer Graphics and Image Processing Faculty of Informatics, University of Debrecen, 4010, Debrecen, POB 12, Hungary

• Dua, D. and Graff, C. (2019). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.