A novel retinal image segmentation using rSVM boosted convolutional neural network for exudates detection

Ghosh, Swarup Kr; Ghosh, Anupam

doi:10.1016/j.bspc.2021.102785

Cited by 14 publications

(8 citation statements)

References 28 publications

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“…Heidari et al [24] VGG16-based CNN 94.5 Lu et al [25] CNN-based breast cancer detection 82.3 Tang et al [26] Multilayer neural-based network 91.55 Leonard et al [30] Autonomous acquisition system 97 Liu et al [31] DNN 85.4 Zhou et al [33] UNet ++ 82.9 Chen et al [34] DRINet 96.57 Chen and Kuo et al [36] PixelHop: SSL model 99.09 Li et al [39] graph-based saliency method and grabCut-based optimization framework 91. 67 Sudharshan and Raj [40] CNN on Keras 96 Surantha and Wicaksono [41] SVM and Histogram-of-Gradient (HoG) Deep autoencoder with BFC 98.5 Ma et al [56] OCT 96 Ghosh and Ghosh [57] CNN-rSVM 98 Liu et al [58] Naïve Bayes and LMS 100 Harisinghaney et al [60] Naïve Bayes, reverse DBSCAN and KNN algorithm 86.83…”

Section: A Discussionmentioning

confidence: 99%

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A Review on Neural Approaches in Image Processing Applications

Bhalla¹

2023

IJRASET

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Image processing is the manipulation and analysis of a digitalized image; it especially improves the image quality. Also, it yields indispensable facts about the image processing techniques required for image enhancement, restoration, preprocessing, and segmentation. These methods help to provide earlier object detection and prevent further impacts due to segmentation and classification. Many reviews already exist for this problem, but those reviews have presented the analysis of a single framework. Hence, this article on Machine Learning (ML) in image processing review has revealed distinct methodologies with diverse frameworks utilized for object detection. The novelty of this review research lies in finding the best neural network model by comparing its efficiency. For that, ML approaches were compared and reported as the best model. Moreover, different kinds of datasets were used to detect the objects and unknown users or intruders. The execution of each approach is compared based on the performance metrics such as sensitivity, specificity, and accuracy using publically accessible datasets like STARE, DRIVE, ROSE, BRATS, and ImageNet. This article discloses the implementation capacity of distinct techniques implemented for each processing methods like supervised and unsupervised. Finally, the Naïve Bayes and LMS model achieved 100% accuracy as finest. Moreover, this technique has utilized public datasets to verify the efficiency. Hence, the overall review of this article has revealed a method for detecting images effectively.

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Section: A Discussionmentioning

confidence: 99%

“…Ghosh and Ghosh [57] have presented CNN with a ranking SVM (CNN-rSVM) model to segment the blood vessels in the retina images. Hence the combination of deep neural convolution model with support vector classification has afforded the best result by gaining 98% accuracy and 96% sensitivity rate.…”

Section: F Image Enhancementmentioning

confidence: 99%

A Review on Neural Approaches in Image Processing Applications

Bhalla¹

2023

IJRASET

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“…However, SVM method requires suitable of kernel function, and this is one of its drawbacks and limitations. Some authors, such as S. K. Ghosh et al [12] and A. M. Syed et al [13] adopted SVM for EXs features segmentation. A commonly developed SVM have been used to the spatial features of the fundus photography have been extracted from RGB color space and mapped into a single binary features plane by the computing of pixel-by-pixel score.…”

Section: Previous Related Workmentioning

confidence: 99%

Exudates Detection Using Fusion of Histogram-Based Fuzzy C-Means Clustering Based on a New Weight Assignment Scheme

Wisaeng

2023

Preprint

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Diabetic Retinopathy (DR) is one of the earliest signs of a complication of diabetes mellitus and is also a leading cause of visual blindness globally. Recent studies have proposed that exudates (EXs) are the hallmark of DR severity. The present study aims to accurately and automatically detect EXs related features that are difficult to detect in fundus photography in early stages. The proposed method utilizes a Fusion of Histogram-Based Fuzzy C-Means clustering (FHBFCM) based on a New Weight Assignment Scheme (NWAS), and a set of four selected features from stages of pre-processing in order to evolve the detection method. Through the coarse segmentation stage, the optimal parameter of FHBFCM is trained by the features of DR for detection of EXs diseases through a stepwise enhancement process. The histogram-based is used to find the color intensity in each of the pixels and performed to accomplish RGB color information. This RGB color information is used as the initial cluster centers for creating the appropriate region and generating the homogeneous regions by FCM method. Afterwards, the best expression of NWAS is then used for fine segmentation stage. The experimental results show that the proposed method achieves accuracy values of 96.12%, 97.20%, 93.22% for EXs segmentation on DiaretDB0, DiaretDB1, and STARE fundus image datasets respectively. In conclusion, this study provides a new method for early detection of EXs related features with a competitive accuracy and its ability to outperforms the other stateof-the-art by optimizing the detection quality, and possibly reducing the false positive segmentation in a significant way.

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“…For classification-based methods, some methods have been proposed for detection using SVM and Convolutional Neural Networks (CNN). Some authors, such as Ghosh et al [12] and Syed et al [13] adopted SVM for EXs segmentation. With some different ideas, Biswal et al [14] proposed a new approach for the detection of EXs using an encoder-decoder style network termed "Deep Multitask Capsule Neural Network (M-CapsNet)".…”

Section: Related Workmentioning

confidence: 99%

A Novel Soft Clustering Method for Detection of Exudates

Wisaeng¹

2023

Computer Systems Science and Engineering

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One of the earliest indications of diabetes consequence is Diabetic Retinopathy (DR), the main contributor to blindness worldwide. Recent studies have proposed that Exudates (EXs) are the hallmark of DR severity. The present study aims to accurately and automatically detect EXs that are difficult to detect in retinal images in the early stages. An improved Fusion of Histogram-Based Fuzzy C-Means Clustering (FHBFCM) by a New Weight Assignment Scheme (NWAS) and a set of four selected features from stages of pre-processing to evolve the detection method is proposed. The features of DR train the optimal parameter of FHBFCM for detecting EXs diseases through a stepwise enhancement method through the coarse segmentation stage. The histogram-based is applied to find the color intensity in each pixel and performed to accomplish Red, Green, and Blue (RGB) color information. This RGB color information is used as the initial cluster centers for creating the appropriate region and generating the homogeneous regions by Fuzzy C-Means (FCM). Afterward, the best expression of NWAS is used for the delicate detection stage. According to the experiment results, the proposed method successfully detects EXs on the retinal image datasets of Dia-retDB0 (Standard Diabetic Retinopathy Database Calibration level 0), DiaretDB1 (Standard Diabetic Retinopathy Database Calibration level 1), and STARE (Structured Analysis of the Retina) with accuracy values of 96.12%, 97.20%, and 93.22%, respectively. As a result, this study proposes a new approach for the early detection of EXs with competitive accuracy and the ability to outperform existing methods by improving the detection quality and perhaps significantly reducing the segmentation of false positives.

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A novel retinal image segmentation using rSVM boosted convolutional neural network for exudates detection

Cited by 14 publications

References 28 publications

A Review on Neural Approaches in Image Processing Applications

A Review on Neural Approaches in Image Processing Applications

Exudates Detection Using Fusion of Histogram-Based Fuzzy C-Means Clustering Based on a New Weight Assignment Scheme

A Novel Soft Clustering Method for Detection of Exudates

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