• Syed Mahamood Shazuli
• Saravanan

Retinal fundus images offer essential diagnostic data regarding different eye diseases, which include glaucoma, diabetic retinopathy (DR), and macular degeneration. In terms of retinal images, image retrieval is used to search for images that display similar patterns or characteristics like the severity of a specific disease or the presence of certain lesions. On the other hand, Classification is the process of allocating a category or label to an image related to its characteristics or features. Utilizing several methods, both image retrieval and classification of retinal fundus images are carried out, some of them are conventional deep learning (DL) ML techniques, and convolutional neural networks (CNNs). This study introduces an Artificial Fish Swarm Algorithm with Deep Learning Model for Content based Image Retrial and Classification on Retinal Fundus Images (AFSADL-IRC) technique. The presented AFSADL-IRC technique retrieves the fundus images and classifies them using DL model. To attain this, Guided filtering (GF) based pre-processing is applied for noise removal process. Next, the EfficientNet model produces a collection of feature vectors with AFSA based hyperparameter optimizer. To retrieve images, Manhattan Distance metric is used for similarity measurement. Finally, Elman neural network (ENN) model classifies the retrieved images into various classes. The experimental result analysis of the AFSADL-IRC algorithm is tested on retinal fundus image dataset and the outcomes indicate the better performance of the AFSADL-IRC technique compared to existing techniques.

"Artificial Fish Swarm Algorithm with Deep Learning Model for Content based Image Retrial and Classification on Retinal Fundus Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 10, page no.g356-g368, October-2023, Available :http://www.jetir.org/papers/JETIR2310542.pdf

"Artificial Fish Swarm Algorithm with Deep Learning Model for Content based Image Retrial and Classification on Retinal Fundus Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 10, page no. ppg356-g368, October-2023, Available at : http://www.jetir.org/papers/JETIR2310542.pdf