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dc.contributor.authorQahwaji, Rami S.R.*
dc.contributor.authorIpson, Stanley S.*
dc.contributor.authorSharif, Mhd Saeed*
dc.contributor.authorBrahma, A.*
dc.date.accessioned2016-05-06T08:32:04Z
dc.date.available2016-05-06T08:32:04Z
dc.date.issued2015-11
dc.identifier.citationSharif MS, Qahwaji RSR, Ipson SS 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: 269-282.en_US
dc.identifier.urihttp://hdl.handle.net/10454/8303
dc.descriptionYesen_US
dc.description.abstractCorneal images can be acquired using confocal microscopes which provide detailed images of the different layers inside the cornea. Most corneal problems and diseases 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, or evaluating the normal cornea, it is important also to be able to automatically recognise these layers easily. 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 (ANN) and adaptive neuro fuzzy inference systems (ANFIS), are powerful AI techniques, which have the capability to accurately classify the main layers of the cornea. The use of an ANFIS approach to analyse corneal layers is described for the first time in this paper, and statistical features have been also employed in the identification of the corneal abnormality. An ANN approach is then added to form a combined committee machine with improved performance which achieves an accuracy of 100% for some classes in the processed data sets. Three normal data sets of whole corneas, comprising a total of 356 images, and seven abnormal corneal images associated with diseases have been investigated in the proposed system. The resulting system is able to pre-process (quality enhancement, noise removal), classify (whole data sets, not just samples of the images as mentioned in the previous studies), and identify abnormalities in the analysed data sets. The system output is visually mapped and the main corneal layers are displayed. 3D volume visualisation for the processed corneal images as well as for each individual corneal cell is also achieved through this system. Corneal clinicians have verified and approved the clinical usefulness of the developed system especially in terms of underpinning the expertise of ophthalmologists and its applicability in patient care.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttp://dx.doi.org/10.1016/j.asoc.2015.07.019en_US
dc.rights(c) 2015 Elsevier B.V. Full-text reproduced in accordance with the publisher's self-archiving policy.en_US
dc.subjectCornea; Confocal microscopy; Artificial Neural Network; ANFIS; Image classification; Analysisen_US
dc.titleMedical image classification based on artificial intelligence approaches: A practical study on normal and abnormal confocal corneal imagesen_US
dc.status.refereedYesen_US
dc.date.Accepted2015-07-22
dc.date.application2015-07-31
dc.typeArticleen_US
dc.type.versionAccepted Manuscripten_US
refterms.dateFOA2018-07-25T12:12:51Z


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