A modified convolutional neural network architecture for diabetic retinopathy screening using SVDD

Karsaz, Ali

doi:10.1016/j.asoc.2022.109102

Cited by 3 publications

(2 citation statements)

References 59 publications

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“…e proposed method increases the accuracy rate in the detection process, which improves the performance of the diagnosis process. Karsaz [9] introduced a new CNN-based DR detection method. Support vector domain description (SVDD) is used in CNN to classify the features and patterns of DR.…”

Section: Related Workmentioning

confidence: 99%

Pixel-Boundary-Dependent Segmentation Method for Early Detection of Diabetic Retinopathy

Sandhya

Suhasini

2022

Mathematical Problems in Engineering

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Early and precise detection of diabetic retinopathy prevents vision impairments through computer-aided clinical procedures. Identifying the symptoms and processing those by using sophisticated clinical procedures reduces hemorrhage kind of risks. The input diabetic retinopathy images are influenced by using computer vision-based processes for segmentation and classification through feature extractions. In this article, a delimiting segmentation using knowledge learning (DS-KL) is introduced for classifying and detecting exudate regions by using varying histograms. The input image is identified for its histogram changes from the feature-dependent segmentation process. Depending on the training knowledge from multiple inputs with different exudate regions, the segmentation is performed. This segmentation identifies infected and noninfected regions across the delimiting pixel boundaries. The knowledge-learning process stores the newly identified exudate region for training and pixel correlation. The recurrent training improves the segmentation accuracy with precise detection and limited errors.

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Section: Related Workmentioning

confidence: 99%

Pixel-Boundary-Dependent Segmentation Method for Early Detection of Diabetic Retinopathy

Sandhya

Suhasini

2022

Mathematical Problems in Engineering

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“…It can rely on only a small number of support vectors (SVs) on the sphere to obtain a very flexible and accurate description of the domain. SVDD is currently applied to several fields such as industrial anomaly detection [3] and disease detection [4].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection Algorithm Based on Broad Learning System and Support Vector Domain Description

et al. 2022

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Deep neural network-based autoencoders can effectively extract high-level abstract features with outstanding generalization performance but suffer from sparsity of extracted features, insufficient robustness, greedy training of each layer, and a lack of global optimization. In this study, the broad learning system (BLS) is improved to obtain a new model for data reconstruction. Support Vector Domain Description (SVDD) is one of the best-known one-class-classification methods used to solve problems where the proportion of sample categories of data is extremely unbalanced. The SVDD is sensitive to penalty parameters C, which represents the trade-off between sphere volume and the number of target data outside the sphere. The training process only considers normal samples, which leads to a low recall rate and weak generalization performance. To address these issues, we propose a BLS-based weighted SVDD algorithm (BLSW_SVDD), which introduces reconstruction error weights and a small number of anomalous samples when training the SVDD model, thus improving the robustness of the model. To evaluate the performance of BLSW_SVDD model, comparison experiments were conducted on the UCI dataset, and the experimental results showed that in terms of accuracy and F1 values, the algorithm has better performance advantages than the traditional and improved SVDD algorithms.

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Smart detection and diagnosis of diabetic retinopathy using bat based feature selection algorithm and deep forest technique

Modi¹,

Kumar²

2023

Computers & Industrial Engineering

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A modified convolutional neural network architecture for diabetic retinopathy screening using SVDD

Cited by 3 publications

References 59 publications

Pixel-Boundary-Dependent Segmentation Method for Early Detection of Diabetic Retinopathy

Pixel-Boundary-Dependent Segmentation Method for Early Detection of Diabetic Retinopathy

Anomaly Detection Algorithm Based on Broad Learning System and Support Vector Domain Description

Smart detection and diagnosis of diabetic retinopathy using bat based feature selection algorithm and deep forest technique

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