Analysis and classification of malignancy in pancreatic magnetic resonance images using neural network techniques

Computer‐aided diagnosis (CAD) is a computerized way of detecting tumors in MR images. Magnetic resonance imaging (MRI) has been generally used in the diagnosis and detection of pancreatic tumors. In a medical imaging system, soft tissue contrast and noninvasiveness are clear preferences of MRI. Ina...

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Bibliographic Details
Published in:International journal of imaging systems and technology 2019-12, Vol.29 (4), p.399-418
Main Authors: Balasubramanian, Aruna Devi, Murugan, Pallikonda Rajasekaran, Thiyagarajan, Arun Prasath
Format: Article
Language:English
Subjects:
Algorithms
ANN
Artificial neural networks
Classification
Diagnosis
Feature extraction
GLCM features
Image classification
Image detection
Magnetic resonance imaging
magnetic resonance imaging (MRI)
Medical imaging
Neural networks
NMR
Nuclear magnetic resonance
Pancreas
Soft tissues
Support vector machines
SVM
Tumors
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Summary:Computer‐aided diagnosis (CAD) is a computerized way of detecting tumors in MR images. Magnetic resonance imaging (MRI) has been generally used in the diagnosis and detection of pancreatic tumors. In a medical imaging system, soft tissue contrast and noninvasiveness are clear preferences of MRI. Inaccurate detection of tumor and long time consumption are the disadvantages of MRI. Computerized classifiers can greatly renew the diagnosis activity, in terms of both accuracy and time necessity by normal and abnormal images, automatically. This article presents an intelligent, automatic, accurate, and robust method to classify human pancreas MRI images as normal or abnormal in terms of pancreatic tumor. It represents the response of artificial neural network (ANN) and support vector machine (SVM) techniques for pancreatic tumor classification. For this, we extract features from MR images of pancreas using the GLCM method and select the best features using JAFER algorithm. These features are analyzed by five classification techniques: ANN BP, ANN RBF, SVM Linear, SVM Poly, and SVM RBF. We compare the results with benchmark data set of MR brain images. The analytical outcome presents that the two best features used to classify the MR images using ANN BP technique have 98% classification accuracy.
ISSN:0899-9457
1098-1098
DOI:10.1002/ima.22314