Multiple sclerosis (MS) is one of the most common central nervous system (CNS) diseases in humans. The chronic disease is among a group of nervous illnesses that are associated with the destruction of the myelin pods. Myelin damage usually occurs in certain areas such as the optic nerve, brainstem, cerebellum, and white matter of the brain, which results in a set of clinical symptoms. one of the early signs of this disease is the symptoms of visual problems caused by the destruction of retinal layers. in fact, the destruction of retina layers is an eye disease called neuritis optics (ON). Therefore, there is a pressing need for a medical device that presents images of the inner layers of the patient’s retina. Currently this has been accomplished through optical coherence tomography (OCT). OCT has provided significant assistance to specialists in the diagnosis of MS. Numerous papers have suggested that OCT data are more effective in early diagnosis of MS than other methods, such as magnetic resonance imaging (MRI). Because OCT images provide a large amount of information, we need to analyze the images through image segmentation and thickness mapping. The thickness map actually indicates the determination of small amounts of retinal layers, which include thinning or inflammation in patients, including MS patients and Optic Neuritis. Therefore, the thickness map helps specialists to diagnose diseases, including those mentioned. Most research examines patients with ON, MS with ON and without history ON, different image processing methods and software for segmentation of retinal layers and comparing mean thickness The layers were applied to the macula and the nerve head with the help of statistical software. The method introduced in this study is to consider two rotational and nonrotational modes of RNFL, GCIP and RNFL + GCIP layers in the 20 * 20, 30 * 30 and 40 * 40 squares in the macula area around the fovea center as well as the 9 ETDRS sectors, Which have been tested by RF rgb(29, 34, 40); font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 13px; white-space: normal;" Key Words: 1.Machine learning 2.Multiple sclerosis 3.Optic neuritis 4.Optical coherence tomography