Medical image classification based on artificial intelligence approaches: A practical study on normal and abnormal confocal corneal images

Article


Sharif, M., Qahwaji, R., Ipson, S. and Brahma, A. 2015. Medical image classification based on artificial intelligence approaches: A practical study on normal and abnormal confocal corneal images. Applied Soft Computing. 36 (Nov.), pp. 269-282.
AuthorsSharif, M., Qahwaji, R., Ipson, S. and Brahma, A.
Abstract

Corneal images can be acquired using confocal microscopes which provide detailed views of the different layers inside a human cornea. Some corneal problems and diseases can occur in one or more of the main corneal layers: the epithelium, stroma and endothelium. Consequently, for automatically extracting clinical information associated with corneal diseases, identifying abnormality or evaluating the normal cornea, it is important to be able to automatically recognise these layers reliably. Artificial intelligence (AI) approaches can provide improved accuracy over the conventional processing techniques and save a useful amount of time over the manual analysis time required by clinical experts. Artificial neural networks (ANNs), adaptive neuro fuzzy inference systems (ANFIS) and a committee machine (CM) have been investigated and tested to improve the recognition accuracy of the main corneal layers and identify abnormality in these layers. The performance of the CM, formed from ANN and ANFIS, achieves an accuracy of 100% for some classes in the processed data sets. Three normal corneal data sets and seven abnormal corneal images associated with diseases in the main corneal layers have been investigated with the proposed system. Statistical analysis for these data sets is performed to track any change in the processed images. This system is able to pre-process (quality enhancement, noise removal), classify corneal images, identify abnormalities in the analysed data sets and visualise corneal stroma images as well as each individual keratocyte cell in a 3D volume for further clinical analysis.

KeywordsCornea; Confocal microscopy; Artificial neural network; Adaptive neuro fuzzy inference system; Texture features; Image classification
JournalApplied Soft Computing
Journal citation36 (Nov.), pp. 269-282
ISSN1568-4946
1872-9681
Year2015
PublisherElsevier for World Federation on Soft Computing (WFSC)
Accepted author manuscript
License
CC BY-NC-ND
Digital Object Identifier (DOI)doi:10.1016/j.asoc.2015.07.019
Web address (URL)https://doi.org/10.1016/j.asoc.2015.07.019
Publication dates
Print31 Jul 2015
Publication process dates
Deposited06 Mar 2017
Accepted22 Jul 2015
FunderNHS National Innovation Centre
University of Bradford
NHS National Innovation Centre
University of Bradford
Copyright information© 2015 Elsevier
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