RP squeeze U-SegNet model for lesion segmentation and optimization enabled ShuffleNet based multi-level severity diabetic retinopathy classification.

IF 1.1 3区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Network-Computation in Neural Systems Pub Date : 2024-09-25 DOI:10.1080/0954898X.2024.2395375

Zulaikha Beevi Sulaiman

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引用次数: 0

Abstract

In Diabetic Retinopathy (DR), the retina is harmed due to the high blood pressure in small blood vessels. Manual screening is time-consuming, which can be overcome by using automated techniques. Hence, this paper proposed a new method for classifying the multi-level severity of DR. Initially, the input fundus image is pre-processed by Non-local means Denoising (NLMD). Then, lesion segmentation is carried out by the Recurrent Prototypical-squeeze U-SegNet (RP-squeeze U-SegNet). Next, feature extraction is effectuated to mine image-level features. DR is categorized as abnormal or normal by ShuffleNet and it is tuned by Fractional War Royale Optimization (FrWRO), and later, if DR is detected, severity classification is performed. Furthermore, the FrWRO-SqueezeNet obtained the maximum performance with sensitivity of 97%, accuracy of 93.8%, specificity of 95.1%, precision of 91.8%, and F-Measure of 94.3%. The devised scheme accurately visualizes abnormal regions in the fundus images. Also, it has the ability to identify the severity levels of DR effectively, which avoids the progression risk to vision loss and proliferative disease.

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RP 挤压 U-SegNet 模型用于病变分割和优化基于 ShuffleNet 的多级严重性糖尿病视网膜病变分类。

在糖尿病视网膜病变（DR）中，视网膜因小血管内的高血压而受到损害。人工筛查非常耗时，而使用自动化技术则可以克服这一问题。因此，本文提出了一种新方法，用于对糖尿病视网膜病变的严重程度进行多级分类。首先，对输入的眼底图像进行非局部去噪（NLMD）预处理。然后，利用递归原型挤压 U-SegNet （RP-挤压 U-SegNet）进行病变分割。然后，进行特征提取，挖掘图像级特征。通过 ShuffleNet 将 DR 分为异常或正常，并通过 Fractional War Royale Optimization（FrWRO）对其进行调整，之后，如果检测到 DR，则进行严重程度分类。此外，FrWRO-SqueezeNet 获得了最高性能，灵敏度达 97%，准确度达 93.8%，特异度达 95.1%，精确度达 91.8%，F-Measure 达 94.3%。所设计的方案能准确显示眼底图像中的异常区域。此外，它还能有效识别 DR 的严重程度，从而避免恶化为视力丧失和增殖性疾病的风险。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Network-Computation in Neural Systems 工程技术-工程：电子与电气

CiteScore

3.70

自引率

1.30%

发文量

审稿时长

>12 weeks

期刊介绍： Network: Computation in Neural Systems welcomes submissions of research papers that integrate theoretical neuroscience with experimental data, emphasizing the utilization of cutting-edge technologies. We invite authors and researchers to contribute their work in the following areas: Theoretical Neuroscience: This section encompasses neural network modeling approaches that elucidate brain function. Neural Networks in Data Analysis and Pattern Recognition: We encourage submissions exploring the use of neural networks for data analysis and pattern recognition, including but not limited to image analysis and speech processing applications. Neural Networks in Control Systems: This category encompasses the utilization of neural networks in control systems, including robotics, state estimation, fault detection, and diagnosis. Analysis of Neurophysiological Data: We invite submissions focusing on the analysis of neurophysiology data obtained from experimental studies involving animals. Analysis of Experimental Data on the Human Brain: This section includes papers analyzing experimental data from studies on the human brain, utilizing imaging techniques such as MRI, fMRI, EEG, and PET. Neurobiological Foundations of Consciousness: We encourage submissions exploring the neural bases of consciousness in the brain and its simulation in machines.