Microaneurysm detection using deep learning and interleaved freezing

Chudzik, Piotr and Somshubra, Majumdar and Caliva, Francesco and Al-Diri, Bashir and Hunter, Andrew (2018) Microaneurysm detection using deep learning and interleaved freezing. Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105741I (2 March 2018) . ISSN 0277-786X

Full content URL: https://doi.org/10.1117/12.2293520

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Microaneurysm detection using deep learning and interleaved freezing
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Abstract

Diabetes affects one in eleven adults. Diabetic retinopathy is a microvascular complication of diabetes and the
leading cause of blindness in the working-age population. Microaneurysms are the earliest clinical signs of diabetic
retinopathy. This paper proposes an automatic method for detecting microaneurysms in fundus photographies. A
novel patch-based fully convolutional neural network for detection of microaneurysms is proposed. Compared to
other methods that require five processing stages, it requires only two. Furthermore, a novel network fine-tuning
scheme called Interleaved Freezing is presented. This procedure significantly reduces the amount of time needed
to re-train a network and produces competitive results. The proposed method was evaluated using publicly
available and widely used datasets: E-Ophtha and ROC. It outperforms the state-of-the-art methods in terms of
free-response receiver operatic characteristic (FROC) metric. Simplicity, performance, efficiency and robustness
of the proposed method demonstrates its suitability for diabetic retinopathy screening applications.

Additional Information:Event: SPIE Medical Imaging, 2018, Houston, Texas, United States
Keywords:Deep Learning, Fundus Photography, Convolutional Neural Networks, Diabetic Retinopathy
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
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ID Code:31548
Deposited On:10 Apr 2018 08:24

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