Automated Diabetic Macular Edema (DME) analysis using fine tuning with inception-resnet-v2 on oct images

Ravi M. Kamble, Manesh Kokare, Genevieve C.Y. Chan, Oscar Perdomo, Fabio A. González, Henning Müller, Fabrice Mériaudeau

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Accurate detection of diabetic macular edema (DME) is an important task in optical coherence tomography (OCT) images of the eye. A relatively simple and practical approach is proposed in this paper. A pre-trained convolutional neural network (CNN) is fine tuned for a classification of DME versus normal cases. The fine-tuned Inception-Resnet-v2 CNN model can effectively identify pathologies in comparison to classical learning. Experiments were carried out on the publicly available data set of the Singapore Eye Research Institute (SERI). The developed model was also compared to other fine tuned models, such as Resnet-50 and Inception-v3. The proposed method achieved 100% classification accuracy with the Inception-Resnet-v2 model using a leave-one-out cross-validation strategy. For robustness, the model trained on the SERI dataset was tested on another dataset provided by the Chinese University HongKong (CUHK), also with 100% accuracy. The proposed method is a potentially impactful tool for accurately detecting DME vs. normal cases.

Original languageEnglish (US)
Title of host publication2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages442-446
Number of pages5
ISBN (Electronic)9781538624715
DOIs
StatePublished - Jan 24 2019
Externally publishedYes
Event2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 - Kuching, Malaysia
Duration: Dec 3 2018Dec 6 2018

Publication series

Name2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 - Proceedings

Conference

Conference2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018
Country/TerritoryMalaysia
CityKuching
Period12/3/1812/6/18

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Medicine (miscellaneous)
  • Health Informatics
  • Instrumentation

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