Convolutional mixture of experts model: A comparative study on automatic macular diagnosis in retinal optical coherence tomography imaging

• Published in: Journal of medical signals and sensors Vol. 9; no. 1; pp. 1 - 14
• Main Authors: Rasti, Reza, Mehridehnavi, Alireza, Rabbani, Hossein, Hajizadeh, Fedra
• Format: Journal Article
• Language: English
• Published: India Wolters Kluwer - Medknow Publications 01.01.2019; Medknow Publications and Media Pvt. Ltd; Medknow Publications & Media Pvt Ltd; Wolters Kluwer Medknow Publications
• Subjects: Blindness; Comparative analysis; Comparative literature; Computer-aided diagnosis system; convolutional mixture of experts; diagnosis; Edema; ensemble learning; Eye diseases; Macular degeneration; macular diseases; Medical research; optical coherence tomography; Original; Tomography; ensemble learning; Computer-aided diagnosis system; diagnosis; convolutional mixture of experts; optical coherence tomography; macular diseases
• ISSN: 2228-7477; 2228-7477
• DOI: 10.4103/jmss.JMSS_27_17

Background: Macular disorders, such as diabetic macular edema (DME) and age-related macular degeneration (AMD) are among the major ocular diseases. Having one of these diseases can lead to vision impairments or even permanent blindness in a not-so-long time span. So, the early diagnosis of these diseases are the main goals for researchers in the field. Methods: This study is designed in order to present a comparative analysis on the recent convolutional mixture of experts (CMoE) models for distinguishing normal macular OCT from DME and AMD. For this purpose, we considered three recent CMoE models called Mixture ensemble of convolutional neural networks (ME-CNN), Multi-scale Convolutional Mixture of Experts (MCME), and Wavelet-based Convolutional Mixture of Experts (WCME) models. For this research study, the models were evaluated on a database of three different macular OCT sets. 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. 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). 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. For non-aligned retinal OCTs, these values were 93.95% and 95.56%. 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. 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.