Mayfly optimization with deep learning enabled retinal fundus image classification model. (September 2022)

• Record Type: Journal Article
• Authors: Gupta, Indresh Kumar; Choubey, Abha; Choubey, Siddhartha
• Is Part Of: Computers & electrical engineering. Volume 102(2022)
• Publication Date: 2022-09
• Subjects: Retinal fundus images, Retinal diseases, Image processing, Computer vision, Machine learning, Deep transfer learning -- Mayfly optimization, Image processing; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108176
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Retinal fundus images are widely employed to screen for various eye diseases, giving them significant clinical importance. Investigation of medical images has been considerably enhanced by using deep learning (DL) approaches which enables automated learning of the related features for particular tasks rather than handcrafted techniques. In this view, this study develops an Optimal Deep Convolutional Neural Network for Retinal Fundus Image Classification (ODCNN-RFIC) model. The presented technique involves pre-processing in two stages: Guided Filter (GF) and Adaptive Median Filter (AMF). The U-Net technique is employed for image segmentation, allowing the infected regions to be detected appropriately. Additionally, the EfficientNet feature extractor is utilized for generating feature vectors. Finally, the mayfly optimization with kernel extreme learning machine (MFO-KELM) model is applied as a classification model. The experimental values highlighted the superior performance of the proposed method on existing algorithms.