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Authors: João Gonçalves ; Teresa Conceição and Filipe Soares

Affiliation: Fraunhofer Portugal AICOS, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal

ISBN: 978-989-758-353-7

Keyword(s): Convolution Neural Networks, Feature-based Machine Learning, Inter-observer Reliability, Diabetic Retinopathy.

Abstract: The rapid growth of digital data in healthcare demands medical image analysis to be faster, precise and, at the same time, decentralized. Deep Learning (DL) fits well in this scenario, as there is an enormous data to sift through. Diabetic Retinopathy (DR) is one of the leading causes of blindness that can be avoided if detected in early stages. In this paper, we aim to compare the agreement of different machine learning models against the performance of highly trained ophthalmologists (human graders). Overall results show that transfer learning in the renowned CNNs has a strong agreement even in different datasets. This work also presents an objective comparison between classical feature-based approaches and DL for DR classification, specifically, the interpretability of these approaches. The results show that Inception-V3 CNN was indeed the best-tested model across all the performance metrics in distinct datasets, but with lack of interpretability. In particular, this model reaches the accuracy of 89% on the EyePACS dataset. (More)

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Paper citation in several formats:
Gonçalves, J.; Conceição, T. and Soares, F. (2019). Inter-observer Reliability in Computer-aided Diagnosis of Diabetic Retinopathy.In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, ISBN 978-989-758-353-7, pages 481-491. DOI: 10.5220/0007580904810491

@conference{healthinf19,
author={João Gon\c{C}alves. and Teresa Concei\c{C}ão. and Filipe Soares.},
title={Inter-observer Reliability in Computer-aided Diagnosis of Diabetic Retinopathy},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,},
year={2019},
pages={481-491},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007580904810491},
isbn={978-989-758-353-7},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,
TI - Inter-observer Reliability in Computer-aided Diagnosis of Diabetic Retinopathy
SN - 978-989-758-353-7
AU - Gonçalves, J.
AU - Conceição, T.
AU - Soares, F.
PY - 2019
SP - 481
EP - 491
DO - 10.5220/0007580904810491

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