Journal article Open Access

Gradient Descent Based Hyparameter Tuning of Xception Architecture for Diabetic Retinopathy Segmentation and Classification

K. Yazhini; D. Loganathan

Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP)

Diabetic retinopathy (DR) is a widespread difficulty of diabetes and is considered as a main reason for vision loss in all over the globe. Several difficulties of DR can be avoided by controlling blood glucose level and timely medication. In real time, it is difficult to detect the DR and consumes more time in a manual way. This paper introduces a new Gradient Descent (GD) based Hyper parameter tuned Xception model called GD-Xception model to detect and classify DR images in an effective way. The GD-Xception model involves a series of subprocesses namely preprocessing, segmentation, feature extraction and classification. A set of extensive simulation takes place to ensure the effective outcome of the presented GD-Xception model. The presented model is tested using a DR dataset from Kaggle. The extensive experimental study clearly portrayed the superior outcome of the GD-Xception model with the maximum accuracy, sensitivity and specificity of 99.39%, 98.50% and 99.62% respectively.

Files (941.4 kB)
Name Size
941.4 kB Download
Views 51
Downloads 30
Data volume 28.2 MB
Unique views 48
Unique downloads 30


Cite as