Reference : Early Diagnosis of Multiple Sclerosis Using Swept-Source Optical Coherence Tomography...
Scientific journals : Article
Human health sciences : Multidisciplinary, general & others
Computational Sciences
http://hdl.handle.net/10993/49466
Early Diagnosis of Multiple Sclerosis Using Swept-Source Optical Coherence Tomography and Convolutional Neural Networks Trained with Data Augmentation
English
Lopez-Dorado, Almudena []
Ortiz Del Castillo, Miguel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
Saute, Maria []
J. Rodrigo, Maria []
Barea, Rafael []
M. Sanchez-Morla, Eva []
Cavaliere, Carlo []
M. Rodriguez-Ascariz, Jose []
Orduna-Hospital, Elvira []
Boquete, Luciano []
Garcia-Marin, Elena []
27-Dec-2021
Sensors
Multidisciplinary Digital Publishing Institute (MDPI)
Yes
International
1424-8220
1424-8220
Basel
Switzerland
[en] Background: The aim of this paper is to implement a system to facilitate the diagnosis of multiple sclerosis (MS) in its initial stages. It does so using a convolutional neural network (CNN) to classify images captured with swept-source optical coherence tomography (SS-OCT). Methods: SS-OCT images from 48 control subjects and 48 recently diagnosed MS patients have been used. These images show the thicknesses (45 × 60 points) of the following structures: complete retina, retinal nerve fiber layer, two ganglion cell layers (GCL+, GCL++) and choroid. The Cohen distance is used to identify the structures and the regions within them with greatest discriminant capacity. The original database of OCT images is augmented by a deep convolutional generative adversarial network to expand the CNN’s training set. Results: The retinal structures with greatest discriminant capacity are the GCL++ (44.99% of image points), complete retina (26.71%) and GCL+ (22.93%). Thresholding these images and using them as inputs to a CNN comprising two convolution modules and one classification module obtains sensitivity = specificity = 1.0. Conclusions: Feature pre-selection and the use of a convolutional neural network may be a promising, nonharmful, low-cost, easy-to-perform and effective means of assisting the early diagnosis of MS based on SS-OCT thickness data
http://hdl.handle.net/10993/49466
https://doi.org/10.3390/s22010167

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