Fusing results of several deep learning architectures for automatic classification of normal and Diabetic Macular Edema in optical coherence tomography

Chan, Geneviève C. Y. (Universiti Teknologi Petronas, Malaysia) ; Kamble, Ravi (SGGSIE&T, Nanded, India) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Shah, Syed A. A. (COMSATS Institute of Information Technology, Abbottabad , Pakistan) ; Tang, T.B. (Universiti Teknologi Petronas, Malaysia) ; Mériaudeau, Fabrice (Universiti Teknologi Petronas, Malaysia)

Diabetic Macular Edema (DME) is a severe eye disease that can lead to irreversible blindness if it is left untreated. DME diagnosis still relies on manual evaluation from opthalmologists, thus the process is time consuming and diagnosis may be subjective. This paper presents two novel DME detection frameworks: (1) combining features from three pre-trained Convolutional Neural Networks: AlexNet, VggNet and GoogleNet and performing feature space reduction using Principal Component Analysis and (2) a majority voting scheme based on a plurality rule between classifications from AlexNet, VggNet and GoogleNet. Experiments were conducted using Optical Coherence Tomography datasets retrieved from the Singapore Eye Research Institute and the Chinese University Hong Kong. The results are evaluated using a Leave-Two-Patients-Out Cross Validation at the volume level. This method improves DME classification with an accuracy of 93.75%, which is similar to the best algorithms so far on the same data sets.


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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Note: The status of this file is: restricted


 Record created 2018-11-15, last modified 2019-06-11

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