Multi-retinal disease classification by reduced deep learning features

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• 作者：R. Arunkumar ; P. Karthigaikumar
• 关键词：ANN ; Deep learning ; Retina
• 刊名：Neural Computing and Applications
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：28
• 期：2
• 页码：329-334
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Visi
• 出版者：Springer London
• ISSN：1433-3058
• 卷排序：28

文摘

This paper presents the retina-based disease diagnosis through deep learning-based feature extraction method. This process helps in developing automated screening system, which is capable of diagnosing retina for diseases such as age-related molecular degeneration, diabetic retinopathy, macular bunker, retinoblastoma, retinal detachment, and retinitis pigmentosa. Some of these diseases share a common characteristic, which makes the classification difficult. In order to overcome the above-mentioned problem, deep learning feature extraction and a multi-class SVM classifier are used. The main contribution of this work is the reducing the dimension of the features required to classify the retinal disease, which enhances the process of reducing the system requirement as well as good performance.