Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Cloud-Based Cataract Recognition System Using Hybrid Classifier Model Subject The topic of the resource Cataract recognition system; Deep convolutional neural network; Fundus images; Support vector machine classifier; Telemedical solution Description An account of the resource 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. Creator An entity primarily responsible for making the resource Rajavel R.; Nagappan P. Source A related resource from which the described resource is derived Lecture Notes in Electrical Engineering, Vol-1247 LNEE, pp. 511-518. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-6714-4_40" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-6714-4_40</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212940017&amp;doi=10.1007%2F978-981-97-6714-4_40&amp;partnerID=40&amp;md5=1513b00ed71fc515152bbaee0a5404a7" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212940017&amp;doi=10.1007%2f978-981-97-6714-4_40&amp;partnerID=40&amp;md5=1513b00ed71fc515152bbaee0a5404a7</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 18761100; ISBN: 978-981976713-7 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Rajavel R., Christ University, Karnataka, Bengaluru, 560074, India; Nagappan P., Galgotias University, Uttar Pradesh, Greater Noida, 203201, India