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

Jiyeon Kim, Yongseop Han, Woongsup Lee, Taeseen Kang, Seongjin Lee, Kyong Hoon Kim, Yeongseop Lee, Jin Hyun Kim

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)1036-1043
Number of pages8
JournalTransactions of the Korean Institute of Electrical Engineers
Issue number7
Publication statusPublished - 2021 Jul

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.NRF-2020R1A2C1014855), Regional Innovation Strategy (RIS) through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(MOE), and the BK21 plus program through the National Research Foundation (NRF) funded by the Ministry of Education of Korea (No.5120201013786).

Publisher Copyright:
© 2021 The Korean Institute of Electrical Engineers.

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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