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              <text>Diabetic retinopathy detection using convolutional neural networka study</text>
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              <text>Convolutional neural network; Deep learning; Diabetic retinopathy</text>
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              <text>Detection and classification of Diabetic Retinopathy (DR) is a challenging task. Automation of the detection is an active research area in image processing and machine learning. Conventional preprocessing and feature extraction methods followed by classification of a suitable classifier algorithm are the common approaches followed by DR detection. With the advancement in deep learning and the evolution of Convolutional Neural Network (CNN), conventional preprocessing and feature extraction steps are rapidly being replaced by CNN. This paper reviews some of the recent contributions in diabetic retinopathy detection using deep architectures. Further, two architectures are implemented with minor modifications. Experiments are carried out with different sample sizes, and the detection accuracies of the two architectures are compared.  The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.</text>
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              <text>Wahid F.F.; Raju G.</text>
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              <text>Lecture Notes in Networks and Systems, Vol-132, pp. 127-133.</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-981-15-5309-7_13" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-15-5309-7_13&lt;/a&gt;
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              <text>Restricted Access</text>
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              <text>ISSN: 23673370</text>
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              <text>Wahid F.F., Department of Informtion Technology, Kannur University, Kerala, India; Raju G., Department of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, India</text>
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