An Effective Diagnosis of Diabetic Retinopathy Based on 3d Hybrid Squeezenet Architecture

An Effective Diagnosis of Diabetic Retinopathy Based on 3d Hybrid Squeezenet Architecture

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© 2022 by IJETT Journal
Volume-70 Issue-12
Year of Publication : 2022
Author : B. Venkaiahppalaswamy, PVGD Prasad Reddy, Suresh Batha
DOI : 10.14445/22315381/IJETT-V70I12P216

How to Cite?

B. Venkaiahppalaswamy, PVGD Prasad Reddy, Suresh Batha, "An Effective Diagnosis of Diabetic Retinopathy Based on 3d Hybrid Squeezenet Architecture," International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 147-159, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I12P216

Abstract
Diabetic retinopathy (DR), which affects an increasing number of people of all ages who have the disease, causes vision issues. Computer-aided diagnosis technology, particularly for the development of deep learning techniques, has become widely used in research for the screening of DR. Accurate annotation is more expensive than other vision tasks since most deep learning-based DR grading models need many annotations to provide data direction, and it is difficult for professionals to detect tiny lesion locations from fundus images. This paper developed a deep learning-based 3D hybrid squeezenet architecture for multiclass DR classification. Here, 3D CNN and squeeznet are hybridized to detect the classes such as mild, moderate proliferate accurately, and severe DR. In addition, an effective data augmentation/ enhancement is achieved using Gaussian blurring and random shift, as well as their combination. The proposed system for data augmentation in the initial stage is to lessen the overfitting issue in 3D hybrid squeeznet to acquire a more trustworthy and robust classification. In this case, intend to categorize several classes, and constructing a 3-D based hybrid network is a superior design since it will train the data more effectively and with less complexity. Finally, a performance study with and without data augmentation is performed to assess the efficiency of the proposed design. The experimental results of the proposed methodology attain enhanced performance than the different compared techniques in terms of accuracy, precision, recall, F1 score, sensitivity, specificity, kappa score and run time.

Keywords
Diabetic retinopathy, Deep learning, Gaussian blurring, Random shifting, Data augmentation.

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