| # Glaucoma detector [](https://share.streamlit.io/golden-panther/glaucoma-detector/glaucoma_app.py) |
|
|
| Link: https://share.streamlit.io/golden-panther/glaucoma-detector/glaucoma_app.py |
| |
| (If app at above link doesn't work then use this project by downloading and running it on your local PC) |
| |
| ## Web Page |
|  |
| |
| ## Healthy Eye |
|  |
| |
| ## Glaucomatous Eye |
|  |
| |
| # >> Details |
| ## Part 1 |
| * We have collected all the publicly available labelled(glaucoma or normal) fundus images of eye from web. |
| * Some are already cropped and some are full. So, we cropped the full fundus images too. |
| * We bulk renamed all the images in the two classes using https://www.bulkrenameutility.co.uk/ |
| * Then we bulk converted all the images to jpg using https://www.xnview.com/en/xnconvert/ |
| * Number of images in both classes are not equal. They are highly imbalanced. Then we balanced by removing extra images. |
| * Finally, we sticked with 1,115 images of each class totalling 2,230 (Contact me if you want this data). And we divided them randomly into train, val and test sets in the ratio 8:1:1 using https://pypi.org/project/split-folders/ |
| ## Part 2 |
| * We uploaded all these images to my google drive and trained on various CNN architectures from simple to advanced. |
| * We did augmentation of data using keras ImageGenerator to cut down high variance. But there is some bias due to low and bad data. |
| * We used keras (2.4.3) and tesorflow (2.3.0) on top of python (3.6.9). (You can see the code in train.py) |
| * We trained on train set and validated on validation set after each epoch. Finally tested the test set. |
| * It gave 93 percent AUC score, some good accuracy, precision and recall values. We saved the model file(h5) for further usage. |
| * Then we built a simple streamlit app for hosting on web. |
| |
| |
| # >> Usage: |
| |
| Always remember that tensorflow does not support python 3.8. It supports upto version 3.7 only. |
| |
| To use our project - go to this link https://share.streamlit.io/golden-panther/glaucoma-detector/glaucoma_app.py |
|
|
| (or) |
|
|
| To run this app |
|
|
| ``` |
| pip install -r requirements.txt |
| streamlit run https://raw.githubusercontent.com/golden-panther/glaucoma-detector/master/glaucoma_app.py |
| ``` |
|
|
| (or) |
|
|
| To run our glaucoma detector on your machine by cloning this repository, |
| * Type the following in your terminal or cmd: |
| ``` |
| pip install -r requirements.txt |
| streamlit run glaucoma_app.py |
| ``` |
| * The web app opens up in a local host. Then you can use it for classifying. That's it! |
|
|
| * Upload a (jpg) cropped fundus image of eye(if not cropped, see note). Our model predicts whether affected by glaucoma or not. |
| * I provided two folders Glaucomatous and Healthy. These contain images from my test set. Use these if you don't have any fundus images with you. |
|
|
| Note: The image should be cropped around the optic nerve part.(see the below **full to cropped** image for reference) |
|
|
|  |
|
|
