Eye Disease Prediction using Ensemble Learning and Attention on OCT
  Scans
Gauri Naik Nandini Narvekar Dimple Agarwal Nishita Nandanwar Himangi Pande
Eye diseases have posed significant challenges for decades, but advancements
in technology have opened new avenues for their detection and treatment.
Machine learning and deep learning algorithms have become instrumental in this
domain, particularly when combined with Optical Coherent Technology (OCT)
imaging. We propose a novel method for efficient detection of eye diseases from
OCT images. Our technique enables the classification of patients into disease
free (normal eyes) or affected by specific conditions such as Choroidal
Neovascularization (CNV), Diabetic Macular Edema (DME), or Drusen. In this
work, we introduce an end to end web application that utilizes machine learning
and deep learning techniques for efficient eye disease prediction. The
application allows patients to submit their raw OCT scanned images, which
undergo segmentation using a trained custom UNet model. The segmented images
are then fed into an ensemble model, comprising InceptionV3 and Xception
networks, enhanced with a self attention layer. This self attention approach
leverages the feature maps of individual models to achieve improved
classification accuracy. The ensemble model's output is aggregated to predict
and classify various eye diseases. Extensive experimentation and optimization
have been conducted to ensure the application's efficiency and optimal
performance. Our results demonstrate the effectiveness of the proposed approach
in accurate eye disease prediction. The developed web application holds
significant potential for early detection and timely intervention, thereby
contributing to improved eye healthcare outcomes.