Automated diabetic macular edema (DME) analysis using fine tuning with Inception-Resnet-v2 on OCT images

Kamble, Ravi (SGGSIE& T, Nanded, India) ; Chan, Geneviève C. Y. (Universiti Teknologi Petronas, Malaysia) ; Perdomo, Oscar (Universidad Nacional de Colombia) ; González, Fabio A. (Universidad Nacional de Colombia) ; Kokare, Manesh (SGGSIE& T, Nanded, India) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Mériaudeau, Fabrice (Universiti Teknologi Petronas, Malaysia)

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.


Conference Type:
full paper
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Publisher:
Honolulu, USA, 17-21 July 2018
Date:
2018-07
Honolulu, USA
17-21 July 2018
Pagination:
4 p.
Published in:
Proceedings of the 40th International Conference of the IEEE Engineering in Medicine and Biology Society
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 Record created 2018-11-16, last modified 2019-06-11

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