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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
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.😄
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!.
Following files can be found in
920samples) 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.
230samples) 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.
- Prepare a csv containing header as label and predicted value as digit
1representing whether or not the image shows signs of
- The name of above file should be
- Sample submission format available at
Make your first submission here 🚀 !!
🖊 Evaluation Criteria
During evaluation F1 score will be used to test the efficiency of the model where,
- 💪 Challenge Page : https://www.aicrowd.com/challenges/aicrowd-blitz-may-2020/problems/dibrd-predict-diabetic-retinopathy/
- 🗣️ Discussion Forum : https://www.aicrowd.com/challenges/aicrowd-blitz-may-2020/problems/dibrd-predict-diabetic-retinopathy/discussion
- 🏆 leaderboard : https://www.aicrowd.com/challenges/aicrowd-blitz-may-2020/problems/dibrd-predict-diabetic-retinopathy/leaderboards
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.