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- Title
Deep Convolution Features in Non-linear Embedding Space for Fundus Image Classification.
- Authors
Dondeti, Venkatesulu; Bodapati, Jyostna Devi; Shareef, Shaik Nagur; Naralasetti, Veeranjaneyulu
- 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 and deep features are extracted by feeding them to a deep Convolutional Network, Neural Architecture Search Network (NASNet) which searches for the best convolutional layer (or "cell") in NASNet search space. The representations of retinal images in deep space are given as input to the classification model to get the severity level of the disease. The proposed model is applied on the benchmark APTOS 2019 retinal fundus image dataset to evaluate the performance of the proposed model. Our experimental studies indicate that -Support Vector Machine (-SVM) when trained using the projected deep features leads to an improvement in accuracy compared to other machine learning models for fundus image classification. In addition, from the experimental studies we understand that deep features from NASNet give better representation compared to the handcrafted features and features obtained using other projections. We observe that deep features transformed using t-distributed stochastic neighbor embedding (t-SNE) gives more discriminative representations of retinal images and help to achieve an accuracy of 77.90%.
- Publication
Revue d'Intelligence Artificielle, 2020, Vol 34, Issue 3, p307
- ISSN
0992-499X
- Publication type
Academic Journal
- DOI
10.18280/ria.340308