Diabetic Retinopathy Detection using Deep Learning
Retinal diseases such as diabetic retinopathy, glaucoma, and age-related macular degeneration are leading causes of blindness worldwide. Early detection and treatment of these diseases are crucial for preventing vision loss. This project aims to develop an automated retinal disease detection system using machine learning algorithms to analyze fundus images of the retina. The system is built using Python and the Tkinter module for the graphical user interface. A deep residual neural network (ResNet) is trained on a dataset of retinal fundus images to classify images as healthy or diseased. For diseased images, the model further categorizes the disease into classes such as diabetic retinopathy, glaucoma, macular degeneration etc. The trained ResNet model achieves high accuracy in distinguishing normal and abnormal retinal images. The model outputs a disease prediction and classification result for a given input fundus image through a user-friendly interface. By providing quick automated screening, this system can aid in the early diagnosis of retinal abnormalities, allowing for timely treatment and prevention of vision impairment.
Diabetic Retinopathy Detection using CNN
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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