Abstract:
Eye is the most complex and sensitive organs of our body which enables us to visualize things.
Globally, around 1.3 billion people will live with visual impairment. Most eye diseases may
gradually lead to permanent vision loss if not detected and treated early. Glaucoma is a chronic
eye disease often called “silent thief of sight” as it has no symptoms. If it’s not detected at an early
stage, it may cause permanent blindness. Age-related macular degeneration is another chronic eye
disease that causes blurred or reduced central vision leading to blindness. Regular eye health
checkup is required, especially for the elders, to avoid blindness. Tonometry, ophthalmoscopy,
visual field test, and optical coherent tomography are used to diagnose glaucoma and age-related
macular degeneration. Most of the modern imaging devices are expensive and unaffordable for
health facilities in low resource setting. Also, diagnostic methods are manual, time-consuming and
exposed to intra/inter observer variability and require expert supervision.
Most works are focused on binary classification of eye disease. However, grading of the disease
is also an important diagnosis factor that is used to identify the disease progression. Segmenting
cup and disk areas are one of challenging task in the area. Segmentation of OC and OD using
traditional machine learning approach is difficult for extraction of deep feature and learning hidden
pattern by itself which reduce computation time, reliability and performance of the system.
This thesis presented automated glaucoma and AMD detection using a convolutional neural
networks approach from fundus image. Glaucoma detection and grading have been performed
based on the cup to disk ratio calculation after the segmentation of OC and OD features. The
experiments shows that Unet++ achieves state-of-the-art OD and OC segmentation results
compared to Unet. The detection accuracy was found to be 91%. AMD classification has also been
done with 97% accuracy using the VGG16 model.
