A comprehensive diagnosis system for early signs and different diabetic retinopathy grades using fundus retinal images based on pathological changes detection. (November 2020)

• Record Type: Journal Article
• Authors: AbdelMaksoud, Eman; Barakat, Sherif; Elmogy, Mohammed
• Is Part Of: Computers in biology and medicine. Volume 126(2020)
• Publication Date: 2020-11
• Subjects: Multi-label computer-aided diagnostic (CAD) -- Multi-label classification (MLC) -- Diabetic retinopathy (DR) -- Blood vessels (BV) -- Exudates (EX) -- Microaneurysms (MA) -- Hemorrhages (HM); Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2020.104039
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Multi-label classification (MLC) is deemed as an effective and dynamic research topic in the medical image analysis field. For ophthalmologists, MLC benefits can be utilized to detect early diabetic retinopathy (DR) signs, as well as its different grades. This paper proposes a comprehensive computer-aided diagnostic (CAD) system that exploits the MLC of DR grades using colored fundus photography. The proposed system detects and analyzes various retina pathological changes accompanying DR development. We extracted some significant features to differentiate healthy from DR cases as well as differentiate various DR grades. First, we preprocessed the retinal images to eliminate noise and enhance the image quality by using histogram equalization for brightness preservation based on dynamic stretching technique. Second, the images were segmented to extract four pathology variations, which are blood vessels, exudates, microaneurysms, and hemorrhages. Next, six various features were extracted using a gray level co-occurrence matrix, the four extracting areas, and blood-vessel bifurcation points. Finally, the features were supplied to a support vector machine (SVM) classifier to distinguish normal and different DR grades. To train and test the proposed system, we utilized four benchmark datasets (two of them are multi-label datasets) using six performance metrics. The proposed system achieved an average accuracy of 89.2 %, sensitivity of 85.1 %, specificity of 85.2 %, positive predictive value of 92.8 %, area under the curve of 85.2 %, and Disc similarity coefficient (DSC) of 88.7 % . The experiments show promising results as compared with other systems. Highlights: A comprehensive multi-label computer-aided diagnosis system is proposed for differentiating normal from different DR cases using various benchmark datasets. Four main pathological signs of DR are segmented, and six different significant features are extracted from the segmented pathological signs. MLSVM classifier is proposed to detect different DR grades based on the problem transformation. The performance is validated by using six performance metrics (ACC, DSC, AUC, SEN, SPE, PPV). The experiments show promising results as compared with other state-of-the-art techniques.