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The retina is an essential part of the human eye. It is a very small part at the subsequent pole of the human eye, and it is composed of a tissue cell that can detect the presence of light. The tissue is sensitive enough to detect the amount of light present, its intensity, and a range of different wavelengths as well. These tissues generate nerve signals, and those signals are passed to the brain via the optic nerve. If the retina malfunctions, then different retinal disorders can occur such as diabetic retinopathy, glaucoma, and pathologic myopia. These can be considered the major causes of total loss of vision throughout the world.
Usually these diseases are treated by different ophthalmologists and specialist of the fields, but it has been seen that once the disease strikes, it becomes very different and in most of the cases impossible to reverse and gain full vision fitness. Thus, it is of the essence that earlier detection of the disease must be done so that the remedy can work. If the treatment starts in time, vision can be saved. In order to perfectly detect the disease, the ophthalmologists require some quantitative and qualitative analysis of the disease. These readings have to be noted at the start of the detection and throughout the process of the therapy. Depending upon these readings, the ophthalmologists can declare where the patient is heading, toward betterment or toward a worse condition.
The gathering of these qualitative and quantitative metrics through manual methods is insufficient and produces erratic and inconsistent outputs. Therefore, it can be said with a certain degree of confidence that a computerized automated system must be in place to do the job. In this review, a comprehensive analysis and evaluation of practices are accomplished of diverse computer vision and image processing techniques applied to OCT images for an automatic, computer-aided examination for the diagnosis of retinal disorder diseases. Disease origins and causes are also testified, and these can have proved a very basic understanding of the disease and how the computer-aided diagnosis (CAD) system can be made using this knowledge. Therefore, this review can provide a good understanding to analyze visual impairments found in OCT images. This can be of aid to any researcher in the future to design a system for detection retinal diseases.
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