A novel lightweight deep learning approach for simultaneous optic cup and optic disc segmentation in glaucoma...

Area of Science:

• Ophthalmology
• Medical Imaging
• Artificial Intelligence

Background:

• Glaucoma is a leading cause of irreversible vision loss.
• Accurate optic cup (OC) and optic disc (OD) segmentation is vital for glaucoma diagnosis.
• Existing segmentation methods often suffer from low accuracy or high computational cost.

Purpose of the Study:

• To develop a lightweight deep learning architecture for simultaneous OC and OD segmentation.
• To improve the accuracy and reduce the time cost of glaucoma screening.

Main Methods:

• Proposed a novel lightweight deep learning architecture.
• Incorporated fuzzy learning and a multi-layer perceptron to enhance segmentation.
• Focused on simultaneous segmentation of the optic cup and optic disc.

Main Results:

• Achieved superior segmentation accuracy compared to state-of-the-art methods.
• Demonstrated significantly reduced training time.
• Validated the effectiveness of fuzzy learning and multi-layer perceptron integration.

Conclusions:

• The proposed lightweight deep learning model offers an efficient and accurate solution for glaucoma detection.
• This method addresses the limitations of existing approaches in terms of speed and precision.
• The findings support the use of advanced AI techniques in early glaucoma diagnosis and management.