2020
DOI: 10.18280/ria.340308
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Deep Convolution Features in Non-linear Embedding Space for Fundus Image Classification

Abstract: A machine learning model is introduced to recognize the severity level of the Diabetic Retinopathy (DR), a disease observed in the people suffering from diabetes for a long time and is one of the causes of vision loss and blindness. Major objective of this approach is to generate an effective feature representation of the fundus images so that the level of severity can be identified with less effort and using limited number of samples for training. Color fundus images of the retina are collected, preprocessed … Show more

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Cited by 34 publications
(13 citation statements)
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“…Transfer Learning is a prominent Deep Learning approach in which the knowledge gained while training that model using large scale datasets can be transferred to another model specifically designed to solve a similar or related problem [ 17 , 59 ]. Transfer learning strategies often use pre-trained models, which are deep neural network models trained on huge labeled data and whose weights are readily available for usage [ 47 ].…”
Section: Background Studymentioning
confidence: 99%
“…Transfer Learning is a prominent Deep Learning approach in which the knowledge gained while training that model using large scale datasets can be transferred to another model specifically designed to solve a similar or related problem [ 17 , 59 ]. Transfer learning strategies often use pre-trained models, which are deep neural network models trained on huge labeled data and whose weights are readily available for usage [ 47 ].…”
Section: Background Studymentioning
confidence: 99%
“…where and are the mean of all values of the matri Equation (1) always results in a value within 0 and 1; 0 and 1 if they are the same. Since we are working with calculate the coefficients separately for three different cha three channels are 1, then we can conclude that the second -SVM) for DR classification [41]. They extracted deep features using the Neural Architecture Search Network (NASNet) architecture.…”
Section: Dealing With Duplicate Imagesmentioning
confidence: 99%
“…To compare and correlate its performance with the top layer of the following DL architectures (CNN, ReLU, Sigmoid, Softmax Regression function), on analysis and Transfer Learning approach [5][6][7] the lack of a data set for medical imagery is great.…”
Section: Literature Surveymentioning
confidence: 99%