用下采样+深度学习提升糖尿病视网膜病变分级准确率
Diabetic Retinopathy Classification using Downscaling Algorithms and Deep Learning
- 先用多种下采样算法处理图像，再输入多通道Inception V3网络
- 在组合数据集上达到98.7%准确率，优于现有方法
- 适合医疗影像分析、医学图像处理方向的研究者
糖尿病视网膜病变（DR）是通过记录和分类糖尿病患者眼底图像来评估病情严重程度的医学任务，需将眼底图像分为五个阶段。处理该问题的主要挑战是图像尺寸大且不统一。本文提出在将图像输入深度学习网络前，使用多种下采样算法进行预处理。为提升训练与测试效果，融合了Kaggle和印度糖尿病视网膜病变图像数据集。实验基于新型多通道Inception V3架构，并设计了自定义预处理流程。结果以准确率、特异性和敏感性衡量，均优于先前最优方法。
原文摘要 · Abstract (English)
Diabetic Retinopathy (DR) is an art and science of recording and classifying the retinal images of a diabetic patient. DR classification deals with classifying retinal fundus image into five stages on the basis of severity of diabetes. One of the major issue faced while dealing with DR classification problem is the large and varying size of images. In this paper we propose and explore the use of several downscaling algorithms before feeding the image data to a Deep Learning Network for classification. For improving training and testing; we amalgamate two datasets: Kaggle and Indian Diabetic Retinopathy Image Dataset. Our experiments have been performed on a novel Multi Channel Inception V3 architecture with a unique self crafted preprocessing phase. We report results of proposed approach using accuracy, specificity and sensitivity, which outperform the previous state of the art methods. Index Terms: Diabetic Retinopathy, Downscaling Algorithms, Multichannel CNN Architecture, Deep Learning
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