Detecting Cataracts in Retinal Images using Deep Learning
Poster Number: P211
Presentation Time: 05:00 PM - 06:30 PM
Cataracts are the leading cause of blindness worldwide, with diagnosis typically requiring direct examination from an ophthalmologist. The objective of this research is to develop and train a deep learning model to detect cataracts using retinal image scans and evaluate its performance on three image input types: color fundus photos, heat map images, and binary edge maps.
Speaker(s):
Daniel Li, High School Student
National Institutes of Health
Poster Number: P211
Presentation Time: 05:00 PM - 06:30 PM
Cataracts are the leading cause of blindness worldwide, with diagnosis typically requiring direct examination from an ophthalmologist. The objective of this research is to develop and train a deep learning model to detect cataracts using retinal image scans and evaluate its performance on three image input types: color fundus photos, heat map images, and binary edge maps.
Speaker(s):
Daniel Li, High School Student
National Institutes of Health
Detecting Cataracts in Retinal Images using Deep Learning
Category
High School Scholars
Description
Date: Monday (11/11)
Time: 05:00 PM to 06:30 PM
Room: Grand Ballroom (Posters)
Time: 05:00 PM to 06:30 PM
Room: Grand Ballroom (Posters)