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Adaptive isomap feature extractive gradient deep belief network classifier for diabetic retinopathy identification

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Abstract

Diabetic retinopathy (DR) is an infection that bases eternal visualization loss in patients with diabetes mellitus. With DR, the glucose level in the blood increases, as well as its viscosity, this results in fluid leakage into surrounding tissues in the retina. In other words, DR represents the pathology of capillaries and venules in the retina with leakage effects, being the main acute retinal disorder caused by diabetes. Many DR detection methods have been previously discussed by different researchers; however, accurate DR detection with a reduced execution time has not been achieved by existing methods. The proposed method, the Shape Adaptive box linear filtering-based Gradient Deep Belief network classifier (SAGDEB) Model, is performed to enhance the accuracy of DR detection. The objective of the SAGDEB Model is to perform an efficient DR identification with a higher accuracy and lower execution time. This model comprises three phases: pre-processing, feature extraction, and classification. The shape adaptive box linear filtering image pre-processing is carried out to reduce the image noise without removing significant parts of image content. Then, an isomap geometric feature extraction is performed to compute features of different natures, like shape, texture, and color, from the pre-processed images. After that, the Adaptive gradient Tversky Deep belief network classifier is to perform classification. The deep belief network is probabilistic and generative graphical model that consists of multiple layers such as one input unit, three hidden units, and one output unit. The extracted image featuresare considered in the input layer and these images are sent to hidden layers. Tversky similarity index is applied in hidden layer 1 to analyze the extracted features with testing features. Regarding the similarity value, the sigmoid activation function is determined in hidden layer 2 so different levels of DR can be identified. Finally, the adaptive gradient method is applied in hidden layer 3 to minimize the error. Finally, the classification results are obtained at the output layer. The results were achieved by using a retinal image dataset and the following metrics were analyzed: peak signal-to-noise ratio (PSNR), DR detection accuracy, error rate, and DR detection time. The quantitative analysis results show that the proposed SAGDEB model achieves a higher 6% PSNR, 5% DR detection accuracy, and a lesser 46% error rate, and 13% DR detection time as compared to the two existing methods.

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The data used to support the findings of this study are publicly available.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India

    Alka Singh & Rakesh Kumar

  2. Faculty of Engineering & Information Technology, University of Technology Sydney, Sydney, NSW, Australia

    Amir H. Gandomi

  3. University Research and Innovation Center (EKIK), Óbuda University, 1034, Budapest, Hungary

    Amir H. Gandomi

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  1. Alka Singh
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  2. Rakesh Kumar
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Contributions

Formal analysis, Writing, Visualization, Writing—original draft, Alka Singh.

Investigation, Resources, Supervision, Conceptualization, Methodology,

Writing—review & editing, Investigation, Supervision, Rakesh Kumar.

Funding acquisition, Writing–review & editing, Conceptualization, Methodology, Investigation, Supervision, Amir H Gandomi.

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Correspondence to Alka Singh.

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Singh, A., Kumar, R. & Gandomi, A.H. Adaptive isomap feature extractive gradient deep belief network classifier for diabetic retinopathy identification. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19216-6

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