Multi-class multi-label ophthalmological disease detection using transfer learning based convolutional neural network. (April 2021)

• Record Type: Journal Article
• Authors: Gour, Neha; Khanna, Pritee
• Is Part Of: Biomedical signal processing and control. Volume 66(2021)
• Publication Date: 2021-04
• Subjects: Ophthalmological Disease Detection -- Multi-class Classification -- Multi-label Classification -- Fundus Imaging -- Convolutional Neural Networks; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.102329
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Fundus imaging is a retinal image modality for capturing anatomical structures and abnormalities in the human eye. Fundus images are the primary tool for observation and detection of a wide range of ophthalmological diseases. Changes in and around the anatomical structures like blood vessels, optic disc, fovea, and macula indicate the presence of disease like diabetic retinopathy, glaucoma, age-related macular degeneration (AMD), myopia, hypertension, and cataract. The patient may be suffering from more than one ophthalmological disease observed in either or both the eyes. Two models are proposed for multi-class multi-label fundus images classification of ophthalmological diseases using transfer learning based convolutional neural network (CNN) approaches. Ocular Disease Intelligent Recognition (ODIR) database having fundus images of left and right eye of patients for eight categories is used for experimentation. Four different pre-trained CNN architectures with two different optimizers are used and it is observed that VGG16 pre-trained architecture with SGD optimizer performs better for multi-class multi-label fundus images classification on ODIR database. Highlights: An automated multi-class multi-label fundus image classification method is proposed. Two transfer learning-based models with four CNN architectures are proposed for fundus images. The results are evaluated on grand challenge database named ODIR database. The paper focuses on AUC and F1-score parameters to evaluate the performance.