Cloud-Based Cataract Recognition System Using Hybrid Classifier Model

Title

Cloud-Based Cataract Recognition System Using Hybrid Classifier Model

Creator

Rajavel R.; Nagappan P.

Description

One of the key challenges of ophthalmologists is to diagnose the various ranges of ophthalmological illnesses such as diabetic retinopathy, cataract, and glaucoma. Here, cataract disease is identified as the one of the leading and most common ophthalmological problems that occurs due to aging. A computer-assisted cataract detection and diagnosis support system is required by the ophthalmologists to overcome the error that occurs during manual screening process. So, a cloud-based cataract recognition system is proposed using the convolutional neural network with support vector machine classifier model to improve the prediction accuracy, sensitivity, specificity, precision, recall, F1-score, and Mathews correlation coefficient. Moreover, the four-layer convolutional neural network is finetuned with a rich set of features and trained with various network models such as Inception V3, MobileNet, VGG-16, VGG-19, and ResNet-101. Therefore, the proposed hybrid combination of ResNet-101 with support vector machine classifier makes better cataract detection and outperforms the existing classifier models in terms of above-mentioned performance evaluation metrics. Moreover, the proposed hybrid approach provides the better telemedical solution to remote people by providing accurate disease prediction and severity grading such as normal, mild, premature, and severe cataract. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Source

Lecture Notes in Electrical Engineering, Vol-1247 LNEE, pp. 511-518.

Date

2024-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Cataract recognition system; Deep convolutional neural network; Fundus images; Support vector machine classifier; Telemedical solution

Coverage

Rajavel R., Christ University, Karnataka, Bengaluru, 560074, India; Nagappan P., Galgotias University, Uttar Pradesh, Greater Noida, 203201, India

Rights

Restricted Access

Relation

ISSN: 18761100; ISBN: 978-981976713-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-97-6714-4_40

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212940017&doi=10.1007%2f978-981-97-6714-4_40&partnerID=40&md5=1513b00ed71fc515152bbaee0a5404a7