An Ensemble Approach Using ResNet and DenseNet for Cataract Detection

Title

An Ensemble Approach Using ResNet and DenseNet for Cataract Detection

Creator

Jenefa, J.; Vinodha, D.; Sambandam, Rakoth Kandan; Vetriveeran, Divya

Description

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.

Source

Lecture Notes in Networks and Systems;Volume;1221;pp.513-525

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Artificial intelligence; Health care; Machine learning; Medical images; Visual impairment

Coverage

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

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-981960923-9;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-96-0924-6_40

https://www.scopus.com/pages/publications/105006920431?origin=resultslist