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 demonstrate its suitability for diabetic retinopathy screening applications.
Piotr Chudzik, Somshubra Majumdar, Francesco Caliva, Bashir Al-Diri, and Andrew Hunter, "Microaneurysm detection using deep learning and interleaved freezing," Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105741I (Presented at SPIE Medical Imaging: February 13, 2018; Published: 2 March 2018); https://doi.org/10.1117/12.2293520.
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