Diabetic Retinopathy Detection
Retinal image analysis using machine learning and computer vision techniques has been an active research area over the past two decades. The emergence of deep learning has significantly advanced the automation of medical image analysis and disease detection.
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.
This 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. Data augmentation techniques are used to expand the size and diversity of the dataset.
Retinal Disease Classification using Deep Learning
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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