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 Faculty Publications Book Chapter Faculty Publications- Book Chapter 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/. Creator An entity primarily responsible for making the resource Jenefa, J.; Sambandam, Rakoth Kandan; Vinodha, D.; Vetriveeran, Divya; Balakrishnan, Sivaneasan Title A name given to the resource Deep Learning-Based Approach for Automated Cataract Detection Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Deep Learning Models towards Health Informatics Management: Foundations, Challenges and Opportunities;pp.117-130 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003678007-7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003678007-7</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105026950840?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105026950840?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jenefa J., Christ University, Bangalore, India; Sambandam R.K., Christ University, Bangalore, India; Vinodha D., Christ University, Bangalore, India; Vetriveeran D., Christ University, Bangalore, India; Balakrishnan S., Singapore Institute of Technology, 10 Dover Drive, Singapore Description An account of the resource Advancements in deep learning approaches is of profound significance in the early detection of cataracts. Automated cataract detection using deep learning approaches is proposed in this chapter. Initially, two pretrained custom convolutional neural network (CNN) architectures, VGG-19 and MobileNetV2, were implemented to detect cataracts. ODIR-5K dataset is used for training, testing, and validating these models, and it has almost 6,400 fundus images. This preprocessed dataset provides the metadata of the available images and is labeled with diagnostic keywords. Since the dataset is highly imbalanced, class weighting techniques are utilized to avoid the impact of the imbalanced dataset. The performance of the models is evaluated, and results show that the ensemble approach outperforms other pretrained models, demonstrating the efficacy of hybrid CNN architecture in enhancing the accuracy of the diagnosis process. 2026 selection and editorial matter, T. Ananth Kumar, R. Rajmohan, M. Niranjanamurthy and G. Sambasivam. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104045118-2; 978-100367800-7; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online