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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/6992
Title: A package-SFERCB-"Segmentation, feature extraction, reduction and classification analysis by both SVM and ANN for brain tumors"
Authors: Sachdeva J.
Kumar V.
Gupta I.
Khandelwal N.
Ahuja C.K.
Published in: Applied Soft Computing Journal
Abstract: The objective of this experimentation is to develop an interactive CAD system for assisting radiologists in multiclass brain tumor classification. The study is performed on a diversified dataset of 428 post contrast T1-weighted MR images of 55 patients and publically available dataset of 260 post contrast T1-weighted MR images of 10 patients. The first dataset includes primary brain tumors such as Astrocytoma (AS), Glioblastoma Multiforme (GBM), childhood tumor-Medulloblastoma (MED) and Meningioma (MEN), along with secondary tumor-Metastatic (MET). The second dataset consists of Astrocytoma (AS), Low Grade Glioma (LGL) and Meningioma (MEN). The tumor regions are marked by content based active contour (CBAC) model. The regions are than saved as segmented regions of interest (SROIs). 71 intensity and texture feature set is extracted from these SROIs. The features are specifically selected based on the pathological details of brain tumors provided by the radiologist. Genetic Algorithm (GA) selects the set of optimal features from this input set. Two hybrid machine learning models are implemented using GA with support vector machine (SVM) and artificial neural network (ANN) (GA-SVM and GA-ANN) and are tested on two different datasets. GA-SVM is proposed for finding preliminary probability in identifying tumor class and GA-ANN is used for confirmation of accuracy. Test results of the first dataset show that the GA optimization technique has enhanced the overall accuracy of SVM from 79.3% to 91.7% and of ANN from 75.6% to 94.9%. Individual class accuracies delivered by GA-SVM are: AS-89.8%, GBM-83.3%, MED-95.6%, MEN-91.8%, and MET-97.1%. Individual class accuracies delivered by GA-ANN classifier are: AS-96.6%, GBM-86.6%, MED-93.3%, MEN-96%, MET-100%. Similar results are obtained for the second dataset. The overall accuracy of SVM has increased from 80.8% to 89% and that of ANN has increased from 77.5% to 94.1%. Individual class accuracies delivered by GA-SVM are: AS-85.3%, LGL-88.8%, MEN-93%. Individual class accuracies delivered by GA-ANN classifier are: AS-92.6%, LGL-94.4%, MED-95.3%. It is observed from the experiments that GA-ANN classifier has provided better results than GA-SVM. Further, it is observed that along with providing finer results, GA-SVM provides advantage in speed whereas GA-ANN provides advantage in accuracy. The combined results from both the classifiers will benefit the radiologists in forming a better decision for classifying brain tumors.
Citation: Applied Soft Computing Journal (2016), 47(): 151-167
URI: https://doi.org/10.1016/j.asoc.2016.05.020
http://repository.iitr.ac.in/handle/123456789/6992
Issue Date: 2016
Publisher: Elsevier Ltd
Keywords: Brain tumors
GA-ANN
GA-SVM
Genetic Algorithm (GA)
ISSN: 15684946
Author Scopus IDs: 54999212000
25646515800
56211916300
7006950739
23048455900
Author Affiliations: Sachdeva, J., Department of Electrical and Instrumentation Engineering, Thapar University, Patiala, India
Kumar, V., Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Gupta, I., Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Khandelwal, N., Department of Radio Diagnosis, Post Graduate Institute of Medical Education and Research, Chandigarh, India
Ahuja, C.K., Department of Radio Diagnosis, Post Graduate Institute of Medical Education and Research, Chandigarh, India
Corresponding Author: Sachdeva, J.; Department of Electrical and Instrumentation Engineering, Thapar UniversityIndia; email: jainysachdeva@gmail.com
Appears in Collections:Journal Publications [EE]

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