Title:An Artificial Intelligence Driven Approach for Classification of Ophthalmic
Images using Convolutional Neural Network: An Experimental Study
Volume: 20
Author(s): Shagundeep Singh, Raphael Banoub, Harshal A. Sanghvi*, Ankur Agarwal, K.V. Chalam, Shailesh Gupta and Abhijit S. Pandya
Affiliation:
- Department of CEECS, Florida Atlantic University, FL, USA
- Charles E. Schmidt College of Medicine, Florida Atlantic University, FL, USA
- Department of Technology and Clinical Trials, Advanced Research, FL, USA
Keywords:
Artificial intelligence, Deep learning, Ocular disease, Detection, Diagnosis, Algorithms.
Abstract:
Background:
Early disease detection is emphasized within ophthalmology now more than ever, and as a result, clinicians and innovators turn to deep learning to
expedite accurate diagnosis and mitigate treatment delay. Efforts concentrate on the creation of deep learning systems that analyze clinical image
data to detect disease-specific features with maximum sensitivity. Moreover, these systems hold promise of early accurate diagnosis and treatment
of patients with common progressive diseases. DenseNet, ResNet, and VGG-16 are among a few of the deep learning Convolutional Neural
Network (CNN) algorithms that have been introduced and are being investigated for potential application within ophthalmology.
Methods:
In this study, the authors sought to create and evaluate a novel ensembled deep learning CNN model that analyzes a dataset of shuffled retinal color
fundus images (RCFIs) from eyes with various ocular disease features (cataract, glaucoma, diabetic retinopathy). Our aim was to determine (1) the
relative performance of our finalized model in classifying RCFIs according to disease and (2) the diagnostic potential of the finalized model to
serve as a screening test for specific diseases (cataract, glaucoma, diabetic retinopathy) upon presentation of RCFIs with diverse disease
manifestations.
Results:
We found adding convolutional layers to an existing VGG-16 model, which was named as a proposed model in this article that, resulted in
significantly increased performance with 98% accuracy (p<0.05), including good diagnostic potential for binary disease detection in cataract,
glaucoma, diabetic retinopathy.
Conclusion:
The proposed model was found to be suitable and accurate for a decision support system in Ophthalmology Clinical Framework.
