ItemBook Chapter

Deep Learning-Based Approach for Automated Cataract Detection

Title
Deep Learning-Based Approach for Automated Cataract Detection
Creator
Jenefa, J.; Sambandam, Rakoth Kandan; Vinodha, D.; Vetriveeran, Divya; Balakrishnan, Sivaneasan
Description
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.
Source
Deep Learning Models towards Health Informatics Management: Foundations, Challenges and Opportunities;pp.117-130
Date
01-01-2026
Publisher
CRC Press
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 978-104045118-2; 978-100367800-7;
Format
online
Language
English
Type
Book chapter

Citation

Jenefa, J.; Sambandam, Rakoth Kandan; Vinodha, D.; Vetriveeran, Divya; Balakrishnan, Sivaneasan, “Deep Learning-Based Approach for Automated Cataract Detection,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 17, 2026, https://archives.christuniversity.in/items/show/24464.

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