https://github.com/javaidiqbal11/blindness-detection-from-images-using-machine-learning

This repo was developed for the blindness detection using machine learning on the image dataset.
https://github.com/javaidiqbal11/blindness-detection-from-images-using-machine-learning

blindness-detection image-processing machine-learning medical-imaging supervised-machine-learning

Last synced: about 1 month ago
JSON representation

This repo was developed for the blindness detection using machine learning on the image dataset.

• Host: GitHub
• URL: https://github.com/javaidiqbal11/blindness-detection-from-images-using-machine-learning
• Owner: javaidiqbal11
• Created: 2023-09-20T07:54:51.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-09-20T08:03:18.000Z (over 1 year ago)
• Last Synced: 2023-09-20T09:29:53.279Z (over 1 year ago)
• Topics: blindness-detection, image-processing, machine-learning, medical-imaging, supervised-machine-learning
• Language: HTML
• Homepage: https://www.jtech.com.pk/
• Size: 4.6 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
## BLINDNESS DETECTION USING MACHINE LEARNING APPROACHES

Vision impairment and blindness are chronic diseases where blindness is a complete or partial loss of

vision. Blindness occurs suddenly or over a period. The primary reasons for blindness occurrence are

diabetes and secondly eye diseases. Older people in developing countries are more affected than other

age people. The major problem with blindness is no proper guidelines or precautions for the people.

According to the World Health Organization (WHO), 2.2 Billion people are suffering from near or distance

impairment. Due to age, the leading causes are uncorrected refractive errors and cataracts. Diabetes

patients mostly face vision problems due to diabetic retinopathy. The high blood glucose level in the eye

blood vessels increases the chances of vision problems. We proposed the model using deep learning

algorithms to detect blindness in the early stages. We apply pre-processing approaches to manage the

dataset. Then apply ResNet, DenseNet, Xception, and InceptionResNet models to train the model. The

trained model was used for the testing and evaluate the proposed model using accuracy, precision,

recall, and f1-score. The proposed model outperformed using the ResNet model compared to the other

models. This model can be utilized for clinical purposes after testing on different datasets. The proposed

model evaluated for accuracy, precision, recall, and f-measure are 0.93, 0.94, 0.98, and 0.94

respectively. The results show proposed model outperforms blindness detection. 

### How to run the code

- Upload the code file into google drive

- Open the google collaboratory and run each code cell

- You can use Jupiter Notebook