RETRACTED ARTICLE: A combined U-Net and multi-class support vector machine learning models for diabetic retinopathy macula edema segmentation and classification DME

Udayaraju, Pamula; Murthy, K. Sreerama; Jeyanthi, P.; Raju, Bh V S Ramakrishnam; Rajasri, T.; Ramadevi, N.

doi:10.1007/s00500-023-08690-z

Cited by 4 publications

(1 citation statement)

References 37 publications

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“…These hard exudates are yellow with different sizes and shapes. Since diabetic retinopathy is considered as a major concern which affects the people, relies a precise segmentation must be employed to detect and classify the type of diabetic retinopathy ( Atli and Gedik, 2021 ; Udayaraju et al, 2023 ). The researchers were focusing on traditional image processing approaches such as morphological operations and threshold segmentation approaches.…”

Section: Introductionmentioning

confidence: 99%

Level-set based adaptive-active contour segmentation technique with long short-term memory for diabetic retinopathy classification

Bhansali,

Patra,

Abouhawwash

et al. 2023

Front. Bioeng. Biotechnol.

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Diabetic Retinopathy (DR) is a major type of eye defect that is caused by abnormalities in the blood vessels within the retinal tissue. Early detection by automatic approach using modern methodologies helps prevent consequences like vision loss. So, this research has developed an effective segmentation approach known as Level-set Based Adaptive-active Contour Segmentation (LBACS) to segment the images by improving the boundary conditions and detecting the edges using Level Set Method with Improved Boundary Indicator Function (LSMIBIF) and Adaptive-Active Counter Model (AACM). For evaluating the DR system, the information is collected from the publically available datasets named as Indian Diabetic Retinopathy Image Dataset (IDRiD) and Diabetic Retinopathy Database 1 (DIARETDB 1). Then the collected images are pre-processed using a Gaussian filter, edge detection sharpening, Contrast enhancement, and Luminosity enhancement to eliminate the noises/interferences, and data imbalance that exists in the available dataset. After that, the noise-free data are processed for segmentation by using the Level set-based active contour segmentation technique. Then, the segmented images are given to the feature extraction stage where Gray Level Co-occurrence Matrix (GLCM), Local ternary, and binary patterns are employed to extract the features from the segmented image. Finally, extracted features are given as input to the classification stage where Long Short-Term Memory (LSTM) is utilized to categorize various classes of DR. The result analysis evidently shows that the proposed LBACS-LSTM achieved better results in overall metrics. The accuracy of the proposed LBACS-LSTM for IDRiD and DIARETDB 1 datasets is 99.43% and 97.39%, respectively which is comparably higher than the existing approaches such as Three-dimensional semantic model, Delimiting Segmentation Approach Using Knowledge Learning (DSA-KL), K-Nearest Neighbor (KNN), Computer aided method and Chronological Tunicate Swarm Algorithm with Stacked Auto Encoder (CTSA-SAE).

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Section: Introductionmentioning

confidence: 99%