Abstract
Magnetic resonance imaging (MRI) has been used in the diagnosis and detection of pancreas tumor. The disadvantage of MRI is long time-consuming in the manual conclusion by a radiologist. Automated classifiers can update the diagnosis activity, in terms of both accuracy and time necessity. This paper is a trial to use artificial neural network (ANN) and least squares support vector machine (LSSVM) to automatically classify 168 human pancreas MR images under two categories, either normal or abnormal pancreas, and the features were extracted by gray-level cooccurrence matrix (GLCM). ANN plays a vital role, specifically in the application of pancreas tumor detection. Only a few reviews are feasible that lead to the improvement of these algorithms to enhance the diagnosis with respect to specificity and sensitivity. LSSVM is a pattern recognition algorithm which learns to assign labels to objects. Classification accuracy compares with the two methods, ANN and LSSVM. ANN provides the best classification accuracy of 96% compared to LSSVM.
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Acknowledgements
The authors thank the management of Kalasalingam University for providing financial assistance under the scheme of University Research Fellowship (URF). Also, we thank the Department of Electronics and Communication Engineering, Kalasalingam University, Tamil Nadu, India, for permitting to use the computational facilities available in Center for Research in Signal Processing and VLSI Design which was set up with the support of the Department of Science and Technology (DST), New Delhi, under FIST Program in 2013 (Reference No: SR/FST/ETI-336/2013 dated November 2013). Also, we thank KGS Scan center, Madurai for providing the pancreas images without which this work would not have been possible. This database is not publicly available, and we thank Dr. Srinivasan, KGS Scan center, Madurai, for permitting to use the MRI images which are more useful in this research work.
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Aruna Devi, B., Pallikonda Rajasekaran, M. (2019). Performance Evaluation of MRI Pancreas Image Classification Using Artificial Neural Network (ANN). In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 104. Springer, Singapore. https://doi.org/10.1007/978-981-13-1921-1_65
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