Transfer learning for Diabetic Macular Edema (DME) detection on Optical Coherence Tomography (OCT) images

Chan, G.C.Y. and Muhammad, A. and Shah, S.A.A. and Tang, T.B. and Lu, C.-K. and Meriaudeau, F. (2017) Transfer learning for Diabetic Macular Edema (DME) detection on Optical Coherence Tomography (OCT) images. In: UNSPECIFIED.

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Abstract

Diabetic Macular Edema (DME) is a common eye disease that causes irreversible vision loss for diabetic patients, if left untreated. Thus, early diagnosis of DME could help in early treatment and prevent blindness. This paper aims to create a framework based on deep learning for DME recognition on Spectral Domain Optical Coherence Tomography (SD-OCT) images through transfer learning. First, images are pre-processed: denoised using Block-Matching and 3-Dimension (BM3D) filtering and cropped through image boundary extraction. Later, features are extracted using CNN of AlexNet and finally images are classified using SVM classifier. The results are evaluated using 8-fold cross-validation. The experiments show that denoised and cropped images lead to better classification performances, exceeding previous other recent published works of 96 accuracy. © 2017 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: cited By 37; Conference of 5th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017 ; Conference Date: 12 September 2017 Through 14 September 2017; Conference Code:132915
Uncontrolled Keywords: Coherent light; Deep learning; Diagnosis; Image classification; Neural networks; Optical data processing; Optical tomography; Strain measurement; Tomography, Classification performance; Convolutional neural network; Cross validation; Diabetic patient; Image boundaries; Macular edema; Spectral domain optical coherence tomographies; Transfer learning, Image processing
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 09 Nov 2023 16:21
Last Modified: 09 Nov 2023 16:21
URI: https://khub.utp.edu.my/scholars/id/eprint/9099

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