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Automatic Detection of Genetics and Genomics of Eye Disease Using Deep Assimilation Learning Algorithm
Interdisciplinary Sciences: Computational Life Sciences ( IF 4.8 ) Pub Date : 2021-01-04 , DOI: 10.1007/s12539-020-00404-5
Mohamed Yacin Sikkandar 1
Affiliation  

Diabetic retinopathy (DR) is one of the most prevalent genetic diseases in human and it is caused by damage to the blood vessels in the eye retina. If it is undetected and untreated at right time, it can lead to vision loss. There are many medical imaging and processing technologies to improve the diagnostic process of DR to overcome the lack of human experts. In the existing image processing methods, there are issues such as lack of noise removal, improper clustering segmentation and less classification accuracy. This can be accomplished by automatic diagnosis of DR using advanced image processing method. The cotton wool spot (CWS), hard exudates (HE) contains a common manifestation of many diseases in retina including DR and acquired immunodeficiency syndrome. In the present work, super iterative clustering algorithm (SICA) is proposed to identify the CWS, HE on retinal image. Feature-based medical image retrieval (FBMIR) datasets are utilized for this purpose. Noises present on the images and histogram-filtering technique is used to convert red, green, and blue (RGB) images into a perfect greyscale image without noise. After pre-processing, SICA is used to identify the CWS, HE detection on retinal images and eliminates unnecessary areas of interest. In the third stage, after detecting CWS and HE, various statistical features are extracted for further classification using deep assimilation learning algorithm (DALA). The performance of DALA technique is examined with various classification parameters like recall, precision, and F-measure. Finally, the false classification ratios are computed to compare the performance of the trained networks. The proposed method produces accurate detection of affected regions with an accuracy ratio of 98.5% and it is higher than the other conventional methods. This method may improve the accuracy of automatic detection and classification of eye diseases.



中文翻译:

使用深度同化学习算法自动检测眼病的遗传学和基因组学

糖尿病视网膜病变 (DR) 是人类最普遍的遗传疾病之一,它是由眼睛视网膜中的血管损伤引起的。如果没有及时发现和治疗,可能会导致视力下降。有许多医学成像和处理技术可以改善 DR 的诊断过程,以克服人类专家的缺乏。现有的图像处理方法存在噪声去除不足、聚类分割不当、分类准确率低等问题。这可以通过使用先进的图像处理方法自动诊断 DR 来实现。棉絮斑 (CWS)、硬性渗出液 (HE) 是视网膜中许多疾病的常见表现,包括 DR 和获得性免疫缺陷综合征。在目前的工作中,提出了超迭代聚类算法(SICA)来识别视网膜图像上的CWS、HE。为此目的使用了基于特征的医学图像检索 (FBMIR) 数据集。图像上存在的噪声和直方图滤波技术用于将红色、绿色和蓝色 (RGB) 图像转换为没有噪声的完美灰度图像。预处理后,SICA用于识别视网膜图像上的CWS、HE检测并消除不必要的感兴趣区域。第三阶段,在检测到 CWS 和 HE 后,提取各种统计特征以使用深度同化学习算法(DALA)进行进一步分类。DALA 技术的性能通过各种分类参数(如召回率、精度和 F 度量)进行检查。最后,计算错误分类率以比较训练网络的性能。所提出的方法对受影响区域进行准确检测,准确率为98.5%,高于其他传统方法。该方法可以提高眼部疾病自动检测和分类的准确性。

更新日期:2021-01-05
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