Classification of Diabetic Retinopathy through Segmented Microaneurysm Features Using Deep Learning Algorithm
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Date
2024-06-06
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Department of Electronic and Communication Engineering, NITS
Abstract
Diabetes Retinopathy (DR) is a consequence of diabetes that can result in blindness.
Elevated blood sugar levels cause retinal capillaries to shrink, resulting in the production of
tiny, blob-like formations known as microaneurysms (MA). Detecting these little MAs
manually is challenging and usually requires specific training from ophthalmologists. As a
result, there is an urgent need for automated MA detection and DR categorization approaches
to speed up diagnosis and treatment for a greater number of patients. This thesis is divided
into two chapters, both of which focus on DR classification utilizing Machine Learning and
Deep Learning methods. The MA detection procedure typically consists of three stages:
preprocessing, augmentation, and classification. Preprocessing is very important for
improving retinal characteristics and eliminating imaging artifacts, which are a difficult
element of retinal fundus image analysis In this paper, we provide a new Improved Non
Local Mean filter (INLMF) that successfully eliminates image artifacts. Furthermore, we
present an improved U-Net architecture for MA segmentation, in which traditional U-Net
blocks are substituted with customized Res-Net blocks. These Res-Net blocks are made up
of three residual blocks connected by a dropout layer, which provides increased
performance. Our goal is to detect DR early in the presence of MAs. To do this, we provide
a unique deep CNN model that diagnoses DR severity based on the number of MAs present
in fundus pictures, categorizing it as normal, mild, or moderate. Both strategies are carefully
tested on publicly available datasets such as e-ophtha, with our segmentation strategy
outperforming state-of-the-art measures.