Abstract
Diabetic Retinopathy (DR) is a common medical disorder damaging the retinal blood vessels of diabetic patients. Regular screening of fundus images and timely detection of the initial symptoms of DR, namely microaneurysms and hemorrhages, are important to reduce the possibility of vision impairment. The proposed work explores the power of Convolutional Neural Network (CNN) in the analysis and detection of retinal disorders. An automated deep learning model named Deep Convolutional Neural Network-based Diabetic Retinopathy Detection (DCNN-DRD) has been proposed to analyze the retinal images and classify them as healthy or defective based on DR symptoms. A retinal image is fed into the DCNN-DRD model which consists of five convolution and five pooling layers followed by a dropout layer and three fully connected layers. The linear output data produced in every layer represents the weighted value based on DR symptoms and is fed into a gradient descent graph for refinement to improve the learning accuracy through several iterations. Thus, the DCNN-DRD model does not require any preprocessing and learns high-level discriminative features of DR symptoms from the pixel intensities to categorize the retinal image as either healthy or defective. The DCNN-DRD model has been trained with a subset of images from the MESSIDOR dataset and the ROC dataset. Experimental results show that the DCNN-DRD model successfully predicts the retinal image as either healthy or defective with 97% accuracy.
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Acknowledgements
We would like to thank Dr. Ashish Sharma, Lotus Eye Care Hospital, Coimbatore, for his continuous effort in manually grading the retinal images and verifying the results achieved.
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Saranya Rubini, S., Saai Nithil, R., Kunthavai, A., Sharma, A. (2019). Deep Convolutional Neural Network-Based Diabetic Retinopathy Detection in Digital Fundus Images. In: Wang, J., Reddy, G., Prasad, V., Reddy, V. (eds) Soft Computing and Signal Processing . Advances in Intelligent Systems and Computing, vol 900. Springer, Singapore. https://doi.org/10.1007/978-981-13-3600-3_19
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DOI: https://doi.org/10.1007/978-981-13-3600-3_19
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