Department of Computer Science and Engineering, Sri Indu College of Engineering and Technology, Hyderabad, Telangana 501510, India.
Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Telangana 502313, India.
Biomed Res Int. 2022 Jun 7;2022:3163496. doi: 10.1155/2022/3163496. eCollection 2022.
Diabetic patients can also be identified immediately utilizing retinopathy photos, but it is a challenging task. The blood veins visible in fundus photographs are used in several disease diagnosis approaches. We sought to replicate the findings published in implementation and verification of a deep learning approach for diabetic retinopathy identification in retinal fundus pictures. To address this issue, the suggested investigative study uses recurrent neural networks (RNN) to retrieve characteristics from deep networks. As a result, using computational approaches to identify certain disorders automatically might be a fantastic solution. We developed and tested several iterations of a deep learning framework to forecast the progression of diabetic retinopathy in diabetic individuals who have undergone teleretinal diabetic retinopathy assessment in a basic healthcare environment. A collection of one-field or three-field colour fundus pictures served as the input for both iterations. Utilizing the proposed DRNN methodology, advanced identification of the diabetic state was performed utilizing HE detected in an eye's blood vessel. This research demonstrates the difficulties in duplicating deep learning approach findings, as well as the necessity for more reproduction and replication research to verify deep learning techniques, particularly in the field of healthcare picture processing. This development investigates the utilization of several other Deep Neural Network Frameworks on photographs from the dataset after they have been treated to suitable image computation methods such as local average colour subtraction to assist in highlighting the germane characteristics from a fundoscopy, thus, also enhancing the identification and assessment procedure of diabetic retinopathy and serving as a skilled guidelines framework for practitioners all over the globe.
糖尿病患者也可以通过视网膜照片立即识别，但这是一项具有挑战性的任务。眼底照片中可见的血管在几种疾病诊断方法中都有应用。我们试图复制在视网膜眼底照片中识别糖尿病性视网膜病变的深度学习方法的实施和验证中发表的研究结果。为了解决这个问题，建议的研究使用递归神经网络（RNN）从深度网络中提取特征。因此，使用计算方法自动识别某些疾病可能是一个很好的解决方案。我们开发并测试了深度学习框架的几个迭代版本，以预测在基本医疗保健环境中接受远程视网膜糖尿病性视网膜病变评估的糖尿病患者中糖尿病性视网膜病变的进展。单视野或三视野彩色眼底照片集作为两个迭代的输入。利用所提出的 DRNN 方法，使用眼部血管中检测到的 HE 对糖尿病状态进行了高级识别。这项研究表明，复制深度学习方法的发现存在困难，并且需要进行更多的复制和再现研究来验证深度学习技术，特别是在医疗图像处理领域。这项研究探讨了在对数据集的照片进行适当的图像计算方法处理后，如局部平均颜色减除法，利用几种其他深度神经网络框架的应用，以帮助突出从眼底镜中提取相关特征，从而增强糖尿病视网膜病变的识别和评估过程，并作为全球医生的专业指导框架。
