Energy Efficient Learning Algorithms for Glaucoma Diagnosis
- Resource Type
- Conference
- Authors
- Nachnani, Krish
- Source
- 2023 International Conference on Machine Learning and Applications (ICMLA) ICMLA Machine Learning and Applications (ICMLA), 2023 International Conference on. :2033-2038 Dec, 2023
- Subject
- Computing and Processing
Engineering Profession
Robotics and Control Systems
Signal Processing and Analysis
Glaucoma
Machine learning algorithms
Automation
Network topology
Neurons
Generative adversarial networks
Energy efficiency
Deep Learning
Machine Learning
Convolutional Neural Networks
Featurization
- Language
- ISSN
- 1946-0759
Glaucoma is a widespread issue that affects millions of individuals across the globe. A shortage of trained ophthalmologists is just one reason why broad screening is challenging, particularly in rural areas. To address this issue requires not just automation assistance but efficient assistance to match the limited resources in rural areas. This study evaluates energy efficient techniques to classify glaucoma -- techniques that can enable efficient automation assistance for physicians operating in limited environments. Two energy effective detection techniques (MobileNetV2 and machine learning algorithms over featurized data) are evaluated on the ORIGA dataset and the results are compared to variants of ResNet, a state-of-the-art convolutional neural network. While the featurization method exceeds ResNet's performance, MobileNetV2 falls short. This work provides a pathway for energy efficient implementations of glaucoma detection.