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Deep Learning in Medical Image Analysis pp 107–120Cite as

Retinopathy Analysis Based on Deep Convolution Neural Network

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Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1213))

Abstract

At medical checkups or mass screenings, the fundus examination is effective for early detection of systemic hypertension, arteriosclerosis, diabetic retinopathy, etc. In most cases, ophthalmologists and physicians grade retinal images by the condition of the blood vessels, lesions. However, human observation does not provide quantitative results, thus blood vessel analysis is an important process in determining hypertension and arteriosclerosis, quantitatively. This chapter describes the latest automated blood vessel extraction using the deep convolution neural network (DCNN). Diabetic retinopathy is a common cardiovascular disease and a major factor in blindness. Therefore, early detection of diabetic retinopathy is very important to preventing blindness. A microaneurysm is an initial sign of diabetic retinopathy, and much research has been conducted for microaneurysm detection. This chapter also describes diabetic retinopathy detection and automated microaneurysm detection using the DCNN.

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Author information

Authors and Affiliations

  1. University of Shiga Prefecture, Hikone-city, Japan

    Yuji Hatanaka

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  1. Yuji Hatanaka
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Correspondence to Yuji Hatanaka .

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Editors and Affiliations

  1. College of Sciences & Engineering, Flinders University, Adelaide, SA, Australia

    Gobert Lee

  2. Faculty of Engineering, Gifu University, Gifu, Japan

    Hiroshi Fujita

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Hatanaka, Y. (2020). Retinopathy Analysis Based on Deep Convolution Neural Network. In: Lee, G., Fujita, H. (eds) Deep Learning in Medical Image Analysis . Advances in Experimental Medicine and Biology, vol 1213. Springer, Cham. https://doi.org/10.1007/978-3-030-33128-3_7

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