Balamurugan V
- Bangalore, Karnataka, India
- itsbalaaadhi@gmail.com
- +91 90039-24275
Looking for an opportunity to utilise my acquired skills and training to help the company grow. Along with my own growth. I want my efforts to make a considerable difference to the company and help in their consequent success.
Projects
glaucoma detection using cnn ensemble classifier and otsu threshold algorithm
- Machine Learning
- Python
This work develops, trains, and tests a diagnostic tool for glaucoma identification based on imagery of the eye fundus. Segmentation and feature extraction techniques are used to detect glaucoma using retinal images, while other techniques focus on convolutional neural network (CNN) training by brute force. A VCG-16-based Feature Clustering technique is developed in order to accomplish exact vessel segment. The Convolutional Neural Network Ensemble Classifier (CNN-ENSEMBLE) framework has been developed to help identify glaucoma problems early, and the system's accuracy was 98%.
Impact:
- Development of a glaucoma detection system using CNN ensemble classifier
- Improved accuracy and efficiency in glaucoma diagnosis
- Early detection of glaucoma can lead to timely treatment and prevention of vision loss
- Enhances the capabilities of healthcare professionals in diagnosing glaucoma
- Provides a cost-effective and accessible solution for glaucoma screening
- Potential to save lives and improve quality of life for patients with glaucoma
Publications
Glaucoma Detection Using CNN Ensemble Classifier and OTSU Threshold Algorithm
Glaucoma is an eye disorder that damages the optic nerve, impairs vision, and finally renders a person blind. In the field of medicine, glaucoma early diagnosis has taken on significant importance. This work develops, trains, and tests a diagnostic tool for glaucoma identification based on CNN.