Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy

• Published in: Biomedical engineering letters 2018, 8(1), , pp.41-57
• Main Author: Mansour, Romany F.
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial neural networks; Biological and Medical Physics; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; CAI; Classification; Comparative analysis; Computer assisted instruction; Computer simulation; Deep learning; Diabetes; Diabetes mellitus; Diabetic retinopathy; Diagnosis; Discriminant analysis; Engineering; Feature extraction; Gaussian process; Image processing; Image segmentation; Localization; Medical and Radiation Physics; Neural networks; Original; Original Article; Principal components analysis; Retinopathy; 의공학
• ISSN: 2093-9868; 2093-985X
• DOI: 10.1007/s13534-017-0047-y

The high-pace rise in advanced computing and imaging systems has given rise to a new research dimension called computer-aided diagnosis (CAD) system for various biomedical purposes. CAD-based diabetic retinopathy (DR) can be of paramount significance to enable early disease detection and diagnosis decision. Considering the robustness of deep neural networks (DNNs) to solve highly intricate classification problems, in this paper, AlexNet DNN, which functions on the basis of convolutional neural network (CNN), has been applied to enable an optimal DR CAD solution. The DR model applies a multilevel optimization measure that incorporates pre-processing, adaptive-learning-based Gaussian mixture model (GMM)-based concept region segmentation, connected component-analysis-based region of interest (ROI) localization, AlexNet DNN-based highly dimensional feature extraction, principle component analysis (PCA)- and linear discriminant analysis (LDA)-based feature selection, and support-vector-machine-based classification to ensure optimal five-class DR classification. The simulation results with standard KAGGLE fundus datasets reveal that the proposed AlexNet DNN-based DR exhibits a better performance with LDA feature selection, where it exhibits a DR classification accuracy of 97.93% with FC7 features, whereas with PCA, it shows 95.26% accuracy. Comparative analysis with spatial invariant feature transform (SIFT) technique (accuracy—94.40%) based DR feature extraction also confirms that AlexNet DNN-based DR outperforms SIFT-based DR.