应用RDidean评价系统和分类器评价实现模型分析视网膜脱离严重程度

L. Poongothai, K. Sharmila
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引用次数: 0

摘要

视力丧失可能是人类的永久性残疾,这可能归因于视网膜脱离(RD)。这是一种危险的疾病,可能是由于视网膜层的排列失调引起的。脉络膜为光感受器的外部部分提供氧气和营养。如果脉络膜与视网膜分离,视网膜的感光器就会停止工作。对脉络膜的依赖程度很高,因为它为中央凹提供氧气,增加了视网膜血管的透气性。氧气水平的逐渐减少会导致黄斑脱离,对后极的视锥细胞和视杆细胞造成不可逆转的损伤,从而导致失明。如果视网膜迅速重新附着,即使不切除黄斑,也能保持良好的视力。迄今为止,视网膜脱离一直是通过概念数据处理和图像处理技术来研究的。然而,本文通过各种特征来分析个体的视网膜脱离,并将其包含在称为“RDidean”评估系统的方程形式中。因此,评估模型根据从系统中获得的值明确地将视网膜数据库分为正常和异常图像。然后通过多种树分类器模型和MATLAB中的深度学习AlexNet分类器来实现该系统的性能,以了解分类的精度。同时有效地需要另一组算法模型,如SVM变化和Naïve贝叶斯方法来识别评估系统呈现的视网膜脱离严重程度的准确性。本研究通过对焦点的分析,通过眼底彩色图像对正常眼和异常眼进行分类,建立了一个确凿的、完善的预测系统。从而帮助改善临床医生的人体工程学环境,以改进治疗计划,同时提供补充的临床决策,并通过组织视网膜脱离的最佳成本使患者的负担能力制度化。
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Analysis of Retinal Detachment Severity using RDidean Evaluative System and Classifier Assessment Implementation Models
Vision loss can be a permanent disability of a human, that could be attributed to due to Retinal Detachment (RD). This is perilous disorder that could be caused due to dealignment of the layers in a retina. The choroid supplies oxygen and nutrients to the outer segments of the photoreceptors. The retina's photoreceptors will stop working if the choroid separates from the retina. The degree of dependence on the choroid is high, due to its supply of oxygen to the fovea that increases the breathability of the retinal blood vessels. This tapering of the oxygen levels can paramount to macula detachment that can be an irreversible damage to the cones and rods at the posterior pole, thereby leading to blindness. If the retina is quickly reattached, good vision can be preserved even if the macula is not removed. Retinal detachment hitherto has been studied through conceptual data processing and image processing techniques. However, this paper analyzes the retinal detachment for an individual through the various features, and the same is encompassed in an equational form to be termed as the “RDidean” evaluative system. The evaluative model thus explicitly categorizes the retinal database into normal and abnormal images based on the value obtained from the system. The performance of this system is then effectuated through diverse tree classifier models and the deep learning AlexNet classifier in MATLAB to comprehend the precision of classification. While effectively entailing another pool of algorithmic models like the SVM variations and the Naïve Bayesian methods to cognize the accuracy of retinal detachment severity that the evaluative system rendered. This indagation analyzes focusses to establish a corroborative and impeccable prediction system for the classification of normal and abnormal eye through color fundus images. Thereby aiding to improve the ergonomic environment of clinicians to improve the treatment plan, along with delivering complementary clinical decisions, and in institutionalizing affordability for patients through optimal cost for agnizing retinal detachments.
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