Energetic Glaucoma Segmentation and Classification Strategies Using Depth Optimized Machine Learning Strategies.
Energetic Glaucoma Segmentation and Classification Strategies Using Depth Optimized Machine Learning Strategies.
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DOI：
10.1155/2021/5709257
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发表时间：
2021
影响因子：
--
通讯作者：
Thomas P
中科院分区：
文献类型：
--
作者：
Elizabeth Jesi V;Mohamed Aslam S;Ramkumar G;Sabarivani A;Gnanasekar AK;Thomas P
青光眼是一种主要的威胁原因，它会影响视神经，导致个人永久失明。造成青光眼的主要原因有眼压过高、家族史、不规律的睡眠习惯等。这些原因很容易导致青光眼，这种疾病的影响会导致内部视神经系统的严重损害，患者会在几个月内永久失明。这种疾病的主要问题是无法治愈；然而，情感阶段可以减少，并且可以保持与长期相同的影响水平，但这仅在识别的早期阶段才有可能。这种青光眼对眼球造成结构性影响，在常规诊断中很难估计病因。在医学上，青光眼患者的杯盘比（CDR）突然降到最低，严重时会对眼睛造成伤害。青光眼的一般诊断方法是进行光学相干断层扫描（OCT），它捕获眼球的未覆盖部分（背面），是一种有效的可视化眼睛各部分的方法，视神经可见性清晰。OCT图像主要用于青光眼等疾病的识别，准确率较高。本文提出了一种新的青光眼早期识别方法——深度优化机器学习策略（deep Optimized Machine Learning Strategy， DOMLS），该方法采用了一种新的优化逻辑——改进k均值优化逻辑（Modified K-Means optimization logic, MkMOL），以提供最佳的结果准确性，该方法确保准确率水平超过96.2%，错误率最低为0.002%。本文主要研究利用OCT图像对青光眼早期的识别，为人们在青光眼疾病影响下提供一种有效的解决方案。利用基于感兴趣区域（ROI）的光学区域选择方法，可以方便地定位光学杯（OC）和光盘（OD）。本文提出的DOMLS算法证明了青光眼估计的精度水平，并在结果和讨论部分清晰地展示了实际证明。
Glaucoma is a major threatening cause, in which it affects the optical nerve to lead to a permanent blindness to individuals. The major causes of Glaucoma are high pressure to eyes, family history, irregular sleeping habits, and so on. These kinds of causes lead to Glaucoma easily, and the effect of such disease leads to heavy damage to the internal optic nervous system and the affected person will get permanent blindness within few months. The major problem with this disease is that it is incurable; however, the affection stages can be reduced and the same level of effect as that for the long period can be maintained but this is possible only in the earlier stages of identification. This Glaucoma causes structural effect to the eye ball and it is complex to estimate the cause during regular diagnosis. In medical terms, the Cup to Disc Ratio (CDR) is minimized to the Glaucoma patients suddenly and leads to harmful damage to one's eye in severe manner. The general way to identify the Glaucoma is to take Optical Coherence Tomography (OCT) test, in which it captures the uncovered portion of eye ball (backside) and it is an efficient way to visualize diverse portions of eyes with optical nerve visibility shown clearly. The OCT images are mainly used to identify the diseases like Glaucoma with proper and robust accuracy levels. In this work, a new methodology is introduced to identify the Glaucoma in earlier stages, called Depth Optimized Machine Learning Strategy (DOMLS), in which it adapts the new optimization logic called Modified K-Means Optimization Logic (MkMOL) to provide best accuracy in results, and the proposed approach assures the accuracy level of more than 96.2% with least error rate of 0.002%. This paper focuses on the identification of early stage of Glaucoma and provides an efficient solution to people in case of effect by such disease using OCT images. The exact position pointed out is handled by using Region of Interest- (ROI-) based optical region selection, in which it is easy to point the optical cup (OC) and optical disc (OD). The proposed algorithm of DOMLS proves the accuracy levels in estimation of Glaucoma and the practical proofs are shown in the Result and Discussions section in a clear manner.
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