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Multi-Classification of Retinal Diseases Using a Pyramidal Ensemble Deep Framework

conference contribution
posted on 2024-01-19, 04:24 authored by O Akinniyi, Imran RazzakImran Razzak, MM Rahman, H Sandhu, A El-Baz, F Khalifa
Retinal disorders diagnosis is of immense importance for appropriate treatment, i.e., accurate personalized medicine. In this work, a multi-resolutional feature ensemble approach is developed for retinal image classification using optical coherence tomography (OCT) images. Particularly, feature-rich pipeline using pyramidal architecture is designed to extract features from multi-scale inputs using partially-connected networks (PCNet). In addition, higher-order reflectivity features are extracted from the input images and are fused with pyramidal features for classification. The advantage of the hierarchical PCNet structure is that it allowed our system to extract multi-scale information to help in such task, all-at-once classification of the normal and abnormal retina. Namely, the larger input sizes give more global information, while the small inputs focus on local details. Evaluation on public OCT data set of four classes (normal, diabetic macular edema (DME), choroidal neovascularization (CNV), and drusen) and comparison against recent networks demonstrates not only the advantages of the proposed architecture's ability to produce feature-rich classification, but also highlights tangible advantages, such as network parameter reduction, enhanced feature learning and information flow, while reducing the risk of over fitting.

History

Volume

00

Pagination

1945-1949

Location

Kuala Lumpur, Malaysia

Start date

2023-10-08

End date

2023-10-11

ISSN

1522-4880

ISBN-13

9781728198354

Language

eng

Title of proceedings

Proceedings - International Conference on Image Processing, ICIP

Event

2023 IEEE International Conference on Image Processing (ICIP)

Publisher

IEEE

Place of publication

Piscataway, N.J.

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