Title: Salp swarm optimisation with deep transfer learning enabled retinal fundus image classification model

Authors: Indresh Kumar Gupta; Abha Choubey; Siddhartha Choubey

Addresses: Computer Science and Engineering, Shri Shanakaracharya Technical Campus, Bhilai, India ' Computer Science and Engineering, Shri Shanakaracharya Technical Campus, Bhilai, India ' Computer Science and Engineering, Shri Shanakaracharya Technical Campus, Bhilai, India

Abstract: Automated screening and diagnostic process in the healthcare sector improves services, reduces cost and labour. With the developments of machine learning (ML) and deep learning (DL) models, intelligent disease diagnosis models can be designed. Retinal fundus image classification using DL models becomes essential for the identification and classification of distinct retinal diseases. This article develops a salp swarm optimisation with deep transfer learning enabled retinal fundus image classification (SSODTL-RFIC) model. The proposed SSODTL-RFIC model examines the retinal fundus image for the existence of diseases. In addition, a median filtering (MF) approach is employed for the noise removal process and graph cut (GC) segmentation is applied. Besides, MobileNetv1 feature extractor is involved to produce feature vectors. Finally, SSO with cascade forward neural network (CFNN) model is applied for recognition and classification process. A widespread experimentation process is performed on benchmark datasets to examine the enhanced performance of the SSODTL-RFIC model, an extensive comparative examination pointed out the supremacy of the SSODTL-RFIC model over the recent approaches with maximum accuracy of 98.71% and 99.12% on the test ARIA and STARE datasets respectively.

Keywords: retinal fundus images; image classification; machine learning; deep learning; salp swarm algorithm; cascade forward neural network; CFNN.

DOI: 10.1504/IJNVO.2022.127605

International Journal of Networking and Virtual Organisations, 2022 Vol.27 No.2, pp.163 - 180

Received: 28 Mar 2022
Accepted: 07 Jul 2022
Published online: 12 Dec 2022 *

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