Analysis of MRI Images of the Liver, using a Combination of Wavelet and Principle Component Analysis (Pca) and Support Vector Machine (SVM) for the Diagnosis and Classification of Benign and Malignant Tumors
Abstract
Diagnosis of the tumors' tissues in the liver and distinguishing the malignant tumorsfrom benign is a critical issue in medicine. In this regard, so many methods have beenproposed to make the accurate tumor detection and classification algorithms usingMachine Learning and Computer Vision techniques. In this study, first we analyzedthe liver’s MR images using Discrete Wavelet Transform techniques for dimensionality reduction and feature extraction, and then Principal Component Analysis techniquehas been employed to select the essential features for classification, and finally the selected features were used to train Support Vector Machine algorithm. In classification,we used the different kernels for SVM and the result of each classifier was compared.The outcome of the algorithm indicates the high performance of our method whenthere are few training data availableChen EL, Chung PC, Chen CL, Tsai HM, Chang CI. An automatic diagnostic system for CT liver image classification. IEEE Trans Biomed Eng. 1998 Jun;45(6):783-94.
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| Files | ||
| Issue | Vol 10 No 1 (2018) | |
| Section | Original Articles | |
| Keywords | ||
| Liver tumor malignant benign wavelet SVM cross-validation K-fold PCA | ||
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