Abstract
Diabetic retinopathy (DR) is a disease that damages retinal blood vessels and leads to blindness. Usually, colored fundus shots are used to diagnose this irreversible disease. The manual analysis (by clinicians) of the mentioned images is monotonous and error-prone. Hence, various computer vision hands-on engineering techniques are applied to predict the occurrences of the DR and its stages automatically. However, these methods are computationally expensive and lack to extract highly nonlinear features and, hence, fail to classify DR’s different stages effectively. This paper focuses on classifying the DR’s different stages with the lowest possible learnable parameters to speed up the training and model convergence. The VGG16, spatial pyramid pooling layer (SPP) and network-in-network (NiN) are stacked to make a highly nonlinear scale-invariant deep model called the VGG-NiN model. The proposed VGG-NiN model can process a DR image at any scale due to the SPP layer’s virtue. Moreover, the stacking of NiN adds extra nonlinearity to the model and tends to better classification. The experimental results show that the proposed model performs better in terms of accuracy, computational resource utilization compared to state-of-the-art methods.
Highlights
Diabetes is one of the fastest-growing diseases in recent times
A significant eye illness that has been reported due to diabetes mellitus (DM) is known as diabetic retinopathy (DR) [2]–[4]
DATASET DESCRIPTION In our experimental setup, we used the Kaggle1 dataset, which is organized by EyePacs and to the best of our knowledge, it is the largest dataset of fundus images for diabetic retinopathy
Summary
Diabetes is one of the fastest-growing diseases in recent times. Recently, about 382 million people worldwide have diabetes mellitus (DM), and the future projected value of diseases is 592 million by 2025 [1]. Symptoms of DR showed that it produces mutilation of blood vessels in the retina Among those 382 million of the population of the world, 34.6% are reported to be affected by DR. Z. Khan et al.: Diabetic Retinopathy Detection Using VGG-NIN Deep Learning Architecture. In the last stage of NPDR, there are more than 20 intraretinal hemorrhages In response to these damages, new blood vessels are formed, and the phenomenon is called neovascularization, which covers the entire inner surface of the retina. Our focus is to detect all five stages of DR from a given set of fundus images using minimum learning parameters. The rest of the paper is organized as follows: Section II provide the current state-of-the-art in DR and its stages detection using machine learning.
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