Glaucoma, a leading cause of irreversible blindness worldwide, results
from progressive damage to the optic nerve, often linked to elevated
Intra Ocular Pressure (IOP). Early detection is critical for preventing
vision loss, yet traditional diagnostic methods can be limited in
accessibility and effectiveness, particularly in the early stages of the
disease. Addressing the need for early detection, we propose the Deep
Neural Multi-Wavelet Segmentation and ResNet-50 Image
Classification (DNMWS-ResNet-50IC) method, specifically designed
to detect glaucoma at an early stage using retinal fundus images. In
this work, we apply Anisotropic Gaussian Filtering for the
preprocessing of retinal fundus images, effectively reducing image
noise while preserving the original quality of the image pixels by
creating an adaptive window size and scale space. We then utilize multi-
wavelet-based image segmentation, leveraging wavelet transforms to
analyze and decompose the image into its various frequency
components. This technique is particularly advantageous for managing
images with complex structures and textures. Subsequently, the
segmented features are classified using the ResNet-50 model, which
categorizes the images as normal, abnormal, or indicative of early-
stage glaucoma. The effectiveness of the proposed method is assessed
by measuring three key performance indicators sensitivity, specificity,
and accuracy on digital retinal images from the HRF image database.
Additionally, the model's performance is further evaluated on a
separate test set, considering metrics such as accuracy, precision,
recall, and prediction time.
K. Pushpalatha
Coimbatore Institute of Engineering and Technology, India
Irreversible Blindness Intraocular Pressure (IOP), Deep Neural Networks (DNN), Multi-Wavelet Segmentation, ResNet-50, Retinal Fundus Images, Anisotropic Gaussian Filtering, Image Preprocessing, Wavelet Transforms
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 15 , Issue: 1 , Pages: 3338 - 3346 )
Date of Publication :
August 2024
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