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AuthorSalahuddin, Tooba
AuthorQidwai, Uvais
Available date2020-05-15T00:15:05Z
Publication Date2019
Publication NameInternational Journal of Integrated Engineering
ResourceScopus
ISSN2229838X
URIhttp://dx.doi.org/10.30880/ijie.2019.11.03.001
URIhttp://hdl.handle.net/10576/14961
AbstractRecent research shows that small nerve fiber damage is an early detector of neuropathy. These small nerve fibers are present in the human cornea and can be visualized through the use of a corneal confocal microscope. A series of images can be acquired from the subbasal nerve plexus of the cornea. Before the images can be quantified for nerve loss, a human expert manually traces the nerves in the image and then classifies the image as having neuropathy or not. Some nerve tracing algorithms are available in the literature, but none of them are reported as being used in clinical practice. An alternate practice is to visually classify the image for neuropathy without quantification. In this paper, we evaluate the potential of various machine learning techniques for automating corneal nerve image classification. First, the images are down-sampled using discrete wavelet transform, filtering and a number of morphological operations. The resulting binary image is used for extracting characteristic features of the image. This is followed by training the classifier on the extracted features. The trained classifier is then used for predicting the state of the nerves in the images. Our experiments yield a classification accuracy of 0.91 reflecting the effectiveness of the proposed method. Universiti Tun Hussein Onn Malaysia Publisher's Office.
Languageen
PublisherPenerbit UTHM
SubjectCorneal confocal microscopy
Image classification
Machine learning
Medical image analysis
Neuropathy
TitleClassification of corneal nerve images using machine learning techniques
TypeArticle
Issue Number3
Volume Number11
dc.accessType Open Access


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