| dc.description.abstract | Retinal diseases including Age-related Macular Degeneration (AMD), Diabetic Retinopathy
(DR), Cataract, and Myopia, are among the most prevalent causes of irreversible vision loss
worldwide. Early and accurate diagnosis is vital for effective treatment and management, yet
manual assessment of retinal fundus images is time-consuming, subject to inter-observer
variability, and often challenging due to subtle pathological features. Motivated by these
clinical demands, we experimented with the classification of retinal diseases using deep
learning models, starting from conventional deep learning methods and advancing towards a
bilateral ensemble solution.
The first phase of our experimentations involved the use of convolutional neural networks and
vision transformers for multi-class classification using individual ocular fundus images.
Conventional approaches showed promise however they are limited in their capacity to extract
complex features specific to each disease and limited to analysis of single eye which can make
the diagnostic accuracy of these approaches to be unreliable as single eye diagnosis might not
capture the potential correlations of both eyes of the same individual. Such limitations were
suggestive in the experimental scenarios where both eyes presented disease cues, and the
method was restricted in capturing complementary information.
It is in view of such limitations that the next stage of experimentations has made use of various
ensemble learning strategies. Satisfying the need for more potentials, ensemble methods tried
to incorporate a better set of features by combining the predictions of several state-of-the-art
deep learning network architectures. The comparative analysis examined different decision
ensemble techniques as well as feature ensemble techniques, finding out that though the
ensemble methods have improved the overall classification task over conventional models,
there were still certain problems. Most of all, ensemble strategies were still considering images
from each eye as separate entities, ignoring valuable information that are present in both eyes
of a subject affected by the same eye disease.
Our major contribution, based on these insights, is the design and validation of a custom
bilateral ensemble framework for the classification of retinal diseases. This strategy is found
to be unique in combining corresponding fundus images from both eyes of a subject from a
ConvNeXt-XLarge backbone with preprocessing and bilateral feature fusion technique. Using
the disease features in both eyes together, the proposed framework showed a promising
performance for the multiclass disease detection. Comprehensive experiments on the publicly
available OIA-ODIR dataset demonstrated that the bilateral ensemble approach overcomes
conventional method limitations, delivering improved and more reliable results.
The research establishes a foundation for advanced deep learning in detection of eye diseases.
Our work presents that systematic improvements to data presentation and system architecture
can lead to better diagnostic performance and improved potential of artificial intelligence based
systems in the medical field. These findings support future computational ophthalmology
research and practical diagnostic tool development for global eye health. | en_US |
