Medical image classification based on artificial intelligence approaches: A practical study on normal and abnormal confocal corneal images |
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| Affiliation: | 1. School of Electrical Engineering and Computer Science, University of Bradford, Bradford, UK;2. Manchester Royal Eye Hospital, Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK;1. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore;2. Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore 599491, Singapore;3. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia;4. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore;5. Singapore Eye Research Institute, Singapore 168751, Singapore;6. Singapore National Eye Center, Singapore 168751, Singapore;7. Duke-NUS Graduate Medical School, Singapore 169857, Singapore;8. Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore;1. Schepens Eye Research Institute, Massachusetts Eye and Ear, Boston, Massachusetts;2. AbbVie Deutschland GmbH & Co KG, Ludwigshafen, Germany;3. Cornea Research Foundation of America, Indianapolis, Indiana;4. Price Vision Group, Indianapolis, Indiana;1. Department of Mathematics and Computer Science, University of the Balearic Islands, Ctra. de Valldemossa, Km.7.5, 07122 Palma de Mallorca, Spain;2. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain |
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| 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. |
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| Keywords: | Cornea Confocal microscopy Artificial neural network Adaptive neuro fuzzy inference system Texture features Image classification |
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