DEEP LEARNING FOR IMAGE PROCESSING, APPLICATION TO DIABETIC MACULAR EDEMA (DME) DETECTION ON OPTICAL COHERENCE TOMOGRAPHY (OCT) IMAGES - CHAN, GENEVIEVE CHEAU YANN (Universiti Teknologi PETRONAS, 2017)
Diabetic Macular Edema (DME) is a common eye disease which causes irreversible vision loss for diabetic patients, if left untreated. Health care and associated costs for treatment related to eye disease also increases with severity of case. Thus, early diagnosis of DME could help in early treatment and prevent blindness. Using a pretrained network of Convolutional Neural Network (CNN), 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, from a (limited) dataset retrieved from Singapore Eye Research Institute (SERI). The dataset consists of 16 volumes each for normal patients and DME patients with 128 images in each volume.