Comparative analysis of deep learning classifiers for diabetic retinopathy identification and detection

ABSTRACT

Diabetic retinopathy (DR) is a micro vascular problem caused by diabetes that can lead to loss of sight. The early detection of diabetic retinopathy is important to avoid the severity of sightlessness. In this manuscript, a comparative analysis of several deep learning methods for DR identification is proposed. The input fundus images are taken from a standard dataset pre-processed by the Mathematical Morphology process. Moreover, the images are segregated using a Multilevel segmentation of the Region of interest (ROI) based on the split and merge algorithm. After that, an original deep learning architecture is utilized to categorize the segregated fundus images. Deep learning methods, such as Convolution neural network (CNN), Recurrent Neural Network (RNN), Support Vector Machine (SVM), Fuzzy K-means cluster (FKM) and Discriminant Analysis (DA) are proposed to classify the DR. The proposed DR identification and detection with CNN provides 65.54% SP, 100% SE, 78.54% SV and 96.95% ACC. Finally, CNN shows better performance than other classifiers.

KEYWORDS:

• Glaucoma categorization
• segregated pictures
• mathematical morphology technique
• split and merge algorithm
• deep learning architecture

Additional information

Notes on contributors

P. Rayavel

P. Rayavel received his Bachelor's Degree in Computer Science and Engineering from the Anna University in 2007. He received his Master's Degree in Computer Science and Engineering from the Anna University in 2011, Chennai. He is presently working in the Department of Computer Science and Engineering (Cybersecurity) at Sri Sairam Institute of Technology, Chennai, Tamil Nadu, India. His research interests include Medical Image Processing, Machine Learning, Soft Computing and Cloud Computing, He is a Life Member of CSI and an annual member of ISTE.

C. Murukesh

C. Murukesh received his Bachelor's Degree in Electrical and Electronics Engineering from the Bharathiyar University in 2002. He received his Master's Degree in Applied Electronics from Anna University in 2004, Chennai and Ph.D. in Information and Communication Engineering from from Anna University in 2015. He is presently working in the Department of Electronics and Communication Engineering at Velammal Engineering College, Chennai, Tamil Nadu, India. His research interests include Signal Processing, Image Processing, Embedded systems, Machine Learning, Soft Computing and Medical Imaging. He is a Life Member of IEI and an annual member of ISTE.