https://github.com/harmanveer-2546/retinal-disease-classification

The number of visually impaired people worldwide is estimated to be 2.2 billion, of whom at least 1 billion have a vision impairment that could have been prevented or is yet to be addressed. Early detection and diagnosis of ocular pathologies would enable forestall of visual impairment.
https://github.com/harmanveer-2546/retinal-disease-classification

3d-graph classification confusion-matrix densenet dropout keras matplotlib maxpooling2d opencv os plotly retinal-diseases retinal-images seaborn sequential-models tensorflow test-train-split visualization

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The number of visually impaired people worldwide is estimated to be 2.2 billion, of whom at least 1 billion have a vision impairment that could have been prevented or is yet to be addressed. Early detection and diagnosis of ocular pathologies would enable forestall of visual impairment.

• Host: GitHub
• URL: https://github.com/harmanveer-2546/retinal-disease-classification
• Owner: harmanveer-2546
• Created: 2024-08-13T17:56:09.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-08-14T17:20:52.000Z (3 months ago)
• Last Synced: 2024-10-10T08:23:09.154Z (29 days ago)
• Topics: 3d-graph, classification, confusion-matrix, densenet, dropout, keras, matplotlib, maxpooling2d, opencv, os, plotly, retinal-diseases, retinal-images, seaborn, sequential-models, tensorflow, test-train-split, visualization
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/datasets/andrewmvd/retinal-disease-classification/data
• Size: 1.44 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Retinal Disease Classification

Retinal illnesses such as age-related macular degeneration and diabetic macular edema will lead to irreversible blindness. With optical coherence tomography

(OCT), doctors are able to see cross-sections of the retinal layers and provide patients with a diagnosis. Manual reading of OCT images is time-consuming, labor-intensive

and even error-prone. Computer-aided diagnosis algorithms improve efficiency by automatically analyzing and diagnosing retinal OCT images. However, the accuracy and 

interpretability of these algorithms can be further improved through effective feature extraction, loss optimization and visualization analysis.

### About Dataset :

According to the WHO, World report on vision 2019, the number of visually impaired people worldwide is estimated to be 2.2 billion, of whom at least 1 billion 

have a vision impairment that could have been prevented or is yet to be addressed. The world faces considerable challenges in terms of eye care, including inequalities 

in the coverage and quality of prevention, treatment, and rehabilitation services. Early detection and diagnosis of ocular pathologies would enable forestall of visual impairment.

For this purpose, we have created a new Retinal Fundus Multi-disease Image Dataset (RFMiD) consisting of a total of 3200 fundus images captured using three 

different fundus cameras with 46 conditions annotated through adjudicated consensus of two senior retinal experts.