DRDM: Deep Learning Model for Diabetic Retinopathy Detection

Aya Migdady, Omar Alzoubi, Nabil El Kadhi, Samer Shorman
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Abstract

The application of Artificial Intelligence is being applied in the medical industry at a quick pace, and it is currently serving as the main source of support for clinical practice solutions. Clinical practice accuracy could be improved and costs could be decreased with the use of deep learning techniques. To diagnose Diabetic Retinopathy, an effective and dependable method for automatic screening must be identified. However, deep-learning models may face difficulties due to a lack of data in several medical fields. The Diabetic Retinopathy Detection Model (DRDM), a deep learning model, is proposed in this research to identify retinal images as either infected or uninfected. The data transformation approach is used to address the lack of Diabetic Retinopathy data, which helps prevent overfitting by doubling the data. The paper shows that building a highly complex model like EfficientNetB3 or VGG16 is not necessary to achieve high performance, where, the experiment's test results approved that the DRDM model outperforms such pre-trained models. Furthermore, it took much less time for the DRDM model to produce these results.
DRDM:用于糖尿病视网膜病变检测的深度学习模型
人工智能正在快速应用于医疗行业,目前已成为临床实践解决方案的主要支持来源。使用深度学习技术可以提高临床实践的准确性并降低成本。要诊断糖尿病视网膜病变,必须找到一种有效可靠的自动筛查方法。然而,由于缺乏多个医学领域的数据,深度学习模型可能会面临困难。本研究提出了一种深度学习模型--糖尿病视网膜病变检测模型(DRDM),用于将视网膜图像识别为感染或未感染。数据转换方法用于解决糖尿病视网膜病变数据缺乏的问题,通过加倍数据有助于防止过拟合。论文表明,建立像 EfficientNetB3 或 VGG16 这样高度复杂的模型并不是实现高性能的必要条件,实验的测试结果表明 DRDM 模型优于此类预训练模型。此外,DRDM 模型产生这些结果所需的时间要短得多。
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