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      Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database

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          Abstract

          Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNet for an automated detection of multiple retinal diseases with fundus photographs involved in STructured Analysis of the REtina (STARE) database. Dataset was built by expanding data on 10 categories, including normal retina and nine retinal diseases. The optimal outcomes were acquired by using a random forest transfer learning based on VGG-19 architecture. The classification results depended greatly on the number of categories. As the number of categories increased, the performance of deep learning models was diminished. When all 10 categories were included, we obtained results with an accuracy of 30.5%, relative classifier information (RCI) of 0.052, and Cohen’s kappa of 0.224. Considering three integrated normal, background diabetic retinopathy, and dry age-related macular degeneration, the multi-categorical classifier showed accuracy of 72.8%, 0.283 RCI, and 0.577 kappa. In addition, several ensemble classifiers enhanced the multi-categorical classification performance. The transfer learning incorporated with ensemble classifier of clustering and voting approach presented the best performance with accuracy of 36.7%, 0.053 RCI, and 0.225 kappa in the 10 retinal diseases classification problem. First, due to the small size of datasets, the deep learning techniques in this study were ineffective to be applied in clinics where numerous patients suffering from various types of retinal disorders visit for diagnosis and treatment. Second, we found that the transfer learning incorporated with ensemble classifiers can improve the classification performance in order to detect multi-categorical retinal diseases. Further studies should confirm the effectiveness of algorithms with large datasets obtained from hospitals.

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          A comparison of methods for multiclass support vector machines.

          Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using large-scale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much larger optimization problem is required so up to now experiments are limited to small data sets. In this paper we give decomposition implementations for two such "all-together" methods. We then compare their performance with three methods based on binary classifications: "one-against-all," "one-against-one," and directed acyclic graph SVM (DAGSVM). Our experiments indicate that the "one-against-one" and DAG methods are more suitable for practical use than the other methods. Results also show that for large problems methods by considering all data at once in general need fewer support vectors.
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            Estimation of the Youden Index and its associated cutoff point.

            The Youden Index is a frequently used summary measure of the ROC (Receiver Operating Characteristic) curve. It both, measures the effectiveness of a diagnostic marker and enables the selection of an optimal threshold value (cutoff point) for the marker. In this paper we compare several estimation procedures for the Youden Index and its associated cutoff point. These are based on (1) normal assumptions; (2) transformations to normality; (3) the empirical distribution function; (4) kernel smoothing. These are compared in terms of bias and root mean square error in a large variety of scenarios by means of an extensive simulation study. We find that the empirical method which is the most commonly used has the overall worst performance. In the estimation of the Youden Index the kernel is generally the best unless the data can be well transformed to achieve normality whereas in estimation of the optimal threshold value results are more variable.
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              Big Data Deep Learning: Challenges and Perspectives

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                Author and article information

                Contributors
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: Writing – original draft
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: Investigation
                Role: Formal analysis
                Role: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2 November 2017
                2017
                : 12
                : 11
                : e0187336
                Affiliations
                [1 ] Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
                [2 ] Institute of Vision Research, Department of Ophthalmology, Yonsei University College of Medicine, Seoul, South Korea
                [3 ] Department of Electrical & Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada
                Harbin Institute of Technology Shenzhen Graduate School, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-0890-8614
                Article
                PONE-D-17-22218
                10.1371/journal.pone.0187336
                5667846
                29095872
                d595cdf8-5eeb-4fbd-901d-c3ed928adb12
                © 2017 Choi et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 10 June 2017
                : 18 October 2017
                Page count
                Figures: 5, Tables: 2, Pages: 16
                Funding
                This study was supported by a faculty research grant of Yonsei University College of Medicine for 2017 (6-2017-0089).
                Categories
                Research Article
                Medicine and Health Sciences
                Ophthalmology
                Retinal Disorders
                Medicine and Health Sciences
                Geriatrics
                Geriatric Ophthalmology
                Macular Degeneration
                Medicine and Health Sciences
                Ophthalmology
                Geriatric Ophthalmology
                Macular Degeneration
                Medicine and Health Sciences
                Ophthalmology
                Retinal Disorders
                Macular Disorders
                Macular Degeneration
                Medicine and Health Sciences
                Ophthalmology
                Retinal Disorders
                Retinal Degeneration
                Macular Degeneration
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Machine Learning Algorithms
                Research and Analysis Methods
                Simulation and Modeling
                Algorithms
                Machine Learning Algorithms
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Machine Learning Algorithms
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Medicine and Health Sciences
                Ophthalmology
                Retinal Disorders
                Retinopathy
                Diabetic Retinopathy
                Research and Analysis Methods
                Imaging Techniques
                Medicine and Health Sciences
                Ophthalmology
                Retinal Disorders
                Retinopathy
                Hypertensive Retinopathy
                Biology and Life Sciences
                Anatomy
                Ocular System
                Ocular Anatomy
                Retina
                Medicine and Health Sciences
                Anatomy
                Ocular System
                Ocular Anatomy
                Retina
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

                Uncategorized
                Uncategorized

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