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 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/. Creator An entity primarily responsible for making the resource Jenefa, J.; Vinodha, D.; Sambandam, Rakoth Kandan; Vetriveeran, Divya Title A name given to the resource An Ensemble Approach Using ResNet and DenseNet for Cataract Detection Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1221;pp.513-525 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-0924-6_40" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-0924-6_40</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105006920431?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105006920431?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., Department of Computer Science and Engineering, Christ University, Bangalore, India; Vinodha D., Department of Computer Science and Engineering, Christ University, Bangalore, India; Sambandam R.K., Department of Computer Science and Engineering, Christ University, Bangalore, India; Vetriveeran D., Department of Computer Science and Engineering, Christ University, Bangalore, India Description An account of the resource Cataracts represent a widespread ocular condition that profoundly affects an individuals vision and overall quality of life. Timely detection proves crucial for effective treatment, yet existing methodologies often entail invasive and discomforting procedures. Hence, an innovative approach is proposed for cataract detection utilizing an ensemble framework, which presents numerous significant advantages. It uses an ensemble framework amalgamating ResNet and DenseNet pre-trained learning models for cataract detection. This strategy enhances the precision and dependability of diagnosing cataracts. On the other hand, it diminishes false positives and negatives, consequently ensuring more accurate and timely diagnoses. Beyond mere accuracy, our ensemble framework brings about additional benefits. It bolsters the resilience of cataract detection by mitigating the influence of individual model biases and variances. Furthermore, it enhances the systems adaptability, making it applicable to various patient demographics and ocular conditions. Such adaptability is significant in the global healthcare landscape, facilitating effective deployment across diverse regions and populations. Moreover, our approach alleviates the discomfort and invasiveness associated with conventional cataract detection methods, promoting early diagnosis and reducing patient apprehension. Streamlining the diagnostic process also eases the burden on healthcare providers and improves overall patient care. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Artificial intelligence; Health care; Machine learning; Medical images; Visual impairment Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981960923-9; Language A language of the resource English Type The nature or genre of the resource Conference paper 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