A comparative approach for classifying retinal OCT images based on deep learning framework. (19th May 2022)

• Record Type: Journal Article
• Authors: Dureja, Aman; Pahwa, Payal
• Is Part Of: Journal of discrete mathematical sciences & cryptography. Volume 25:Number 4(2022)
• Page Start: 859
• Page End: 870
• Publication Date: 2022-05-19
• Subjects: 68Q01; Convolutional neural network -- Content-based image retrieval -- Medical images -- Augmentation -- OCT images -- Pre-trained model; Computer science -- Mathematics -- Periodicals; Cryptography -- Periodicals; Computer science -- Mathematics; Cryptography; Periodicals; 004.0151
• DOI: 10.1080/09720529.2022.2068595
• Languages: English
• ISSNs: 0972-0529

Abstract

Abstract: Convolutional Networks are category of deep optimizing networks used to interpret images in Deep Learning concepts. Image recognition and medical image analysis are two areas where they are useful. The increasing scale of clinical feature spaces is raising a significant obstacle, creating issues with extensive database management, and afterward compiling those repositories for retrieval and storage, that could only be addressed using content based medical image retrieval systems. The objective of this paper is to demonstrate a deep CNN architecture for retrieving research and clinical images quickly and efficiently for identifying multi-class retinal disease objects. To train the network, the datasets used are inter-modal and divided into 4 groups. The transfer learning method is used for the multi-classification of retinal images. Another augmentation technique is used for comparing the accuracy, precision, and evaluation metrics with the transfer learning method. The accuracy of 97.1%, with a recall of 97.2%, and a precision of 97.0% was achieved in research that is higher when compared with the previous methods that were deployed. With the augmentation technique, it achieved an accuracy of 94.0% with a 94.6% precision and a recall of 95.1% for the testing data which suggests that decreasing the size of data did not impact the accuracy of the model. The proposed model helps diagnose various categories of medical images for the development of a comprehensive system that can work better than the human experts and help to detect and diagnose various diseases in the medical and clinical fields.