Shabbir Amsa, Rasheed Aqsa, Shehraz Huma, Saleem Aliya, Zafar Bushra, Sajid Muhammad, Ali Nouman, Dar Saadat Hanif, Shehryar Tehmina
Department of Software Engineering, Mirpur University of Science and Technology (MUST), Mirpur- AJK 10250, Pakistan.
Department of Computer Science, Government College University, Faisalabad 38000, Pakistan.
Math Biosci Eng. 2021 Mar 2;18(3):2033-2076. doi: 10.3934/mbe.2021106.
Content-based image analysis and computer vision techniques are used in various health-care systems to detect the diseases. The abnormalities in a human eye are detected through fundus images captured through a fundus camera. Among eye diseases, glaucoma is considered as the second leading case that can result in neurodegeneration illness. The inappropriate intraocular pressure within the human eye is reported as the main cause of this disease. There are no symptoms of glaucoma at earlier stages and if the disease remains unrectified then it can lead to complete blindness. The early diagnosis of glaucoma can prevent permanent loss of vision. Manual examination of human eye is a possible solution however it is dependant on human efforts. The automatic detection of glaucoma by using a combination of image processing, artificial intelligence and computer vision can help to prevent and detect this disease. In this review article, we aim to present a comprehensive review about the various types of glaucoma, causes of glaucoma, the details about the possible treatment, details about the publicly available image benchmarks, performance metrics, and various approaches based on digital image processing, computer vision, and deep learning. The review article presents a detailed study of various published research models that aim to detect glaucoma from low-level feature extraction to recent trends based on deep learning. The pros and cons of each approach are discussed in detail and tabular representations are used to summarize the results of each category. We report our findings and provide possible future research directions to detect glaucoma in conclusion.
基于内容的图像分析和计算机视觉技术被应用于各种医疗保健系统中以检测疾病。通过眼底相机拍摄的眼底图像来检测人眼的异常情况。在眼部疾病中，青光眼被认为是导致神经退行性疾病的第二大主要病因。据报道，人眼内眼压不当是这种疾病的主要原因。青光眼在早期没有症状，如果疾病得不到纠正，可能会导致完全失明。青光眼的早期诊断可以预防视力的永久性丧失。人工检查人眼是一种可行的解决方案，但它依赖于人力。通过结合图像处理、人工智能和计算机视觉来自动检测青光眼有助于预防和检测这种疾病。在这篇综述文章中，我们旨在全面综述青光眼的各种类型、青光眼的病因、可能的治疗细节、公开可用的图像基准细节、性能指标以及基于数字图像处理、计算机视觉和深度学习的各种方法。这篇综述文章对各种已发表的研究模型进行了详细研究，这些模型旨在从低级特征提取到基于深度学习的最新趋势来检测青光眼。详细讨论了每种方法的优缺点，并使用表格形式总结了每个类别的结果。最后，我们报告我们的研究结果并提供检测青光眼的可能未来研究方向。
