Eye Disease Detection using OpenCV Python
Diabetic Retinopathy Disease Project aims to develop an intelligent system for automated detection of retinal diseases from fundus images using deep machine learning algorithms. The system is implemented in Python using the Tkinter module for building the graphical user interface. A deep residual neural network (ResNet) is trained on a large dataset of normal and diseased retinal images collected from multiple sources.
Retinal diseases like diabetic retinopathy, glaucoma and macular degeneration are leading causes of preventable vision impairment worldwide. Screening and early detection of these diseases through examination of retinal fundus images is crucial for timely treatment and intervention. However, manual evaluation of these images is tedious, time-consuming and prone to human error, making computer-aided diagnosis highly desirable.
Automated Retinal Disease Screening System using deep machine learning techniques to analyze fundus photographs. A deep residual neural network model was trained on a large curated dataset of normal and diseased retinal images. The model achieved high accuracy in classifying retinal images as normal or abnormal, and could further detect the specific disease type. The user-friendly graphical interface developed allows ophthalmologists to quickly screen patients by uploading fundus images and receiving automated prediction reports.
Retinal Disease Detection using Image Processing
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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