Rasti R, Rabbani H, Mehridehnavi A, Kafieh R. Optical coherence tomography for ultrahigh resolution in vivo imaging. Optical coherence tomography of the human retina. Hee MR, Izatt JA, Swanson EA, Huang D, Schuman JS, Lin CP, et al. Guidelines for the management of neovascular age-related macular degeneration by the European Society of Retina Specialists (EURETINA). Schmidt-Erfurth U, Chong V, Loewenstein A, Larsen M, Souied E, Schlingemann R, et al. Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding. Liu YY, Chen M, Ishikawa H, Wollstein G, Schuman JS, Rehg JM, et al. Clinically significant macular edema and survival in type 1 and type 2 diabetes. Hirai FE, Knudtson MD, Klein BE, Klein R. Age-related macular degeneration is the leading cause of blindness. This allows having a fast and robust computer aided system in macular OCT imaging which does not rely on the routine computerized processes such as denoising, segmentation of retinal layers, and also retinal layers alignment.īressler NM. Conclusion: Based on the comparative analysis, although the MCME model outperformed the other CMoE models in the analysis of aligned retinal OCTs, the WCME offers a robust model for diagnosis of non-aligned retinal OCTs. For non-aligned retinal OCTs, these values were 93.95% and 95.56%. Results: Experimental evaluations showed that the MCME and WCME outperformed the ME-CNN model and presented overall precisions of 98.14% and 96.06% for aligned OCTs respectively. To provide better performance insight into the CMoE ensembles, we extensively analyzed the models based on the 5-fold cross-validation method and various classification measures such as precision and average area under the ROC curve (AUC). Two first OCT sets were acquired by Heidelberg imaging systems consisting of 148 and 45 subjects respectively and set3 was constituted of 384 Bioptigen OCT acquisitions. For this research study, the models were evaluated on a database of three different macular OCT sets. For this purpose, we considered three recent CMoE models called Mixture ensemble of convolutional neural networks (MECNN), Multi-scale Convolutional Mixture of Experts (MCME), and Wavelet-based Convolutional Mixture of Experts (WCME) models. Material and Methods: The present study is designed in order to present a comparative study on the recent convolutional mixture of experts (CMoE) models for distinguishing normal macular OCT from DME and AMD. So, the early diagnosis of these diseases are the main goals of eye researchers. Having one of these diseases can lead to vision impairments or even permanent blindness in a not-so-long time span. Background: Macular disorders, such as diabetic macular edema (DME) and age-related macular degeneration (AMD) are among the major ocular diseases.
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