RETRACTED ARTICLE: ICM-BTD: improved classification model for brain tumor diagnosis using discrete wavelet transform-based feature extraction and SVM classifier

In medical image processing, the detection, classification and segmentation of the tumor region from MRI scans accurately are very complicated, significant and time-consuming process. When there is a scenario occurs to handle with large amount of images for tumor diagnosis, there is need of an effic...

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Bibliographic Details
Published inSoft computing (Berlin, Germany) Vol. 24; no. 24; pp. 18599 - 18609
Main Authors Gokulalakshmi, A., Karthik, S., Karthikeyan, N., Kavitha, M. S.
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2020
Springer Nature B.V
Subjects
Artificial Intelligence
Brain
Brain cancer
Computational Intelligence
Control
Diagnosis
Discrete Wavelet Transform
Engineering
Feature extraction
Image acquisition
Image classification
Image filters
Image processing
Image segmentation
Magnetic resonance imaging
Mathematical Logic and Foundations
Mechatronics
Medical imaging
Methodologies and Application
Neural networks
Robotics
Support vector machines
Tumors
Brain tumor diagnosis
Discrete wavelet transform
Support vector machine
Segmentation
MRI brain images
Classification
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Summary:In medical image processing, the detection, classification and segmentation of the tumor region from MRI scans accurately are very complicated, significant and time-consuming process. When there is a scenario occurs to handle with large amount of images for tumor diagnosis, there is need of an efficient and adaptive classification model to handle with the anomalous structures of human brains. The MRI brain images show the typical internal brain structure and hence help scholars and medical practitioners in accurate disease diagnosis. With that note, this paper develops a model called improved classification model for brain tumor diagnosis for appropriate classification of tumor images from input MRI images. Initially, filtering techniques are applied for preprocessing the acquired scan images and feature extraction is done with gray-level co-occurrence matrix and discrete wavelet transform equations, which produces more precise results. And, classification is done with the technique called support vector machine, in which the binary classifications are effectively done. The proposed model is evaluated under simulation, and the obtained results outperform the results of traditional brain tumor detection process based on precision, recall and processing time.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-020-05096-z