Automated one-hot eye diseases diagnostic framework using deep-learning techniques

Multiple OCT images from the same patient for ophthalmic disease classification, such as AMD, DME, and Drusen, often conflict with each other in classification. The human doctor makes an experience-based medical decision for inconsistent OCT images, but no neural-network-based approach has been proposed to solve the same problem so far. This paper presents a new machine-learning-based framework that makes the comprehensive one-hot decision on AMD, DME, and Drusen, just like human doctors. In this study, we present a two-step deep machine learning method: In the first step, a classical Deep CNN along with transfer learning is used to make an ophthalmic diagnosis for a single OCT image. In the second step, a new framework, we propose, consisting of several supervised deep machine learning methods makes a comprehensive one-hot decision on eye disease from multiple OCT images. In this framework, we developed an AI model that can make comprehensive judgments from inconsistent results obtained from the same patient. Consequently, we could achieve 94% classification accuracy compared to the human doctor classification.