Multiple Sclerosis (MS) is a neurodegenerative disease. As an irreversible disease, the MS screening in early stage is highly recommended. Several works suggested to diagnose MS from the retinal Optical Coherence Tomography (OCT) images, which implies layer thickness. However, a delay on MS screening is registered, caused by the unavailability of medical equipment and the low-rate of medical staff. We propose in this paper a novel method for MS screening from OCT images captured by a pervasive devices. The main challenges is to insure a higher accurate MS screening through a processing workflow executed into smartphone device. For this purpose, we fine tune the convolutive deep neural network “Inception-V4” to extract features from OCT images, which are provided to an Extreme Learning Machine (ELM) classifier to deduce MS disease. A cross-validation process is conducted where 92.00% accuracy, 94.00% sensitivity, 90.00% specificity and 90.38% precision in average are achieved. In addition, the whole method is implemented into a mobile device, where execution time is under one second whatever the OCT image is, which is adequate to employ screening on clinical context.