Optimal Transfer Learning Model for Binary Classification of Funduscopic Images through Simple Heuristics

TL;DR

This paper presents a transfer learning approach using Xception architecture for binary classification of funduscopic images, achieving high accuracy and sensitivity, with a simple heuristic for model optimization and a user-friendly web interface.

Contribution

It introduces a unifying transfer learning model with heuristics for optimal architecture selection and hyperparameters in fundus image classification.

Findings

Achieved 90% accuracy in disease classification

Xception with Adam optimizer performed best

Model accessible via a web interface for broad usability

Abstract

Deep learning models have the capacity to fundamentally revolutionize medical imaging analysis, and they have particularly interesting applications in computer-aided diagnosis. We attempt to use deep learning neural networks to diagnose funduscopic images, visual representations of the interior of the eye. Recently, a few robust deep learning approaches have performed binary classification to infer the presence of a specific ocular disease, such as glaucoma or diabetic retinopathy. In an effort to broaden the applications of computer-aided ocular disease diagnosis, we propose a unifying model for disease classification: low-cost inference of a fundus image to determine whether it is healthy or diseased. To achieve this, we use transfer learning techniques, which retain the more overarching capabilities of a pre-trained base architecture but can adapt to another dataset. For comparisons, we then develop a custom heuristic equation and evaluation metric ranking system to determine the optimal base architecture and hyperparameters. The Xception base architecture, Adam optimizer, and mean squared error loss function perform best, achieving 90% accuracy, 94% sensitivity, and 86% specificity. For additional ease of use, we contain the model in a web interface whose file chooser can access the local filesystem, allowing for use on any internet-connected device: mobile, PC, or otherwise.