Glaucoma Disease Detection using Convolution Neural Network
Objective
The main objective of this project is to detection the glaucoma disease in iris images using transfer learning process.
Abstract
In this application we are focusing on glaucoma disease of an EYE problem. In existing method Machine Learning techniques are used to classify the eye disease but it is failed in providing accurate results. To overcome those problems we are proposing our method using CNN technique. The proposed CNN technique along with transfer learning gives best results when compared with other existing models. The pre-trained model VGG-16 of transfer learning is used that which predict accurately and it also saves the time in build an algorithm/Network.
Keywords: CNN, Transfer learning, Iris images, VGG-16.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
S/W Configuration:
- Operating System : Windows 7/8/10 .
- Server side Script : HTML, CSS & JS.
- IDE : Pycharm.
- Libraries Used : Numpy, IO, OS, Flask, Keras, Tensorflow
- Technology : Python 3.6+.
H/W Configuration:
- RAM : 8GB
- Processor : I3/ Intel processor
- Hard Disk : 160GB
Learning Outcomes
- Scope of Real Time Application Scenarios.
- What is a search engine and how browser can work.
- What type of technology versions are used.
- Use of HTML, and CSS on UI Designs.
- Data Parsing Front-End to Back-End.
- About transfer learning.
- Working Procedure.
- Introduction to basic technologies used for.
- How project works.
- Input and Output modules.
- Practical exposure to
- Hardware and software tools.
- Solution providing for real time problems.
- Working with team/ individual.
- Work on Creative ideas.
- Frame work use.
- About python.
- What is deep learning?
- Deep learning algorithms.
- What is electronic technologies?
- What is image recognition?
- What is convolution neural networks?
Paper Publishing
Request Call Back
Would you like to receive a free callback now?
Choose the best time for callback:
Leave your message and we will contact you as soon as possible
Disclaimer - Takeoff Edu Group Projects, are not associated or affiliated with IEEE, in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, and not projects of or by IEEE.
