Transfer Learning for Detection of Diabetic Retinopathy Disease

Bhupati, Alekhya (2020) Transfer Learning for Detection of Diabetic Retinopathy Disease. Masters thesis, Dublin, National College of Ireland.

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Abstract

Applying deep learning on medical data is a very challenging and crucial task. Transfer learning can reduce the cost of training to a great extent by using pre-trained deep convolution neural networks. Diabetic retinopathy is the major cause of blindness and it is increasing world-wide at an alarming rate. In this work, we proposes to apply the transfer learning methods for detection of diabetic retinopathy disease and its different stages. We have experimented various deep learning models such as VGG19, ResNet50 and DenseNet201 in order to determine the best classification model for DR detection. The large dataset for diabetic retinopathy consists of imbalance dataset. So this experiment has been performed for both balanced and imbalanced dataset. The results of the models has been analyzed using various metrics such as precision, recall, f1-score and accuracy.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software R Medicine > R Medicine (General)
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Dan English
Date Deposited:	23 Jun 2020 12:12
Last Modified:	23 Jun 2020 12:12
URI:	https://norma.ncirl.ie/id/eprint/4316

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