AUTHOR=Veeramuthu A. , Meenakshi S. , Mathivanan G. , Kotecha Ketan , Saini Jatinderkumar R. , Vijayakumar V. , Subramaniyaswamy V. 

TITLE=MRI Brain Tumor Image Classification Using a Combined Feature and Image-Based Classifier

JOURNAL=Frontiers in Psychology

VOLUME=13

YEAR=2022

URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.848784

DOI=10.3389/fpsyg.2022.848784

ISSN=1664-1078

ABSTRACT=<p>Brain tumor classification plays a niche role in medical prognosis and effective treatment process. We have proposed a combined feature and image-based classifier (CFIC) for brain tumor image classification in this study. Carious deep neural network and deep convolutional neural networks (DCNN)-based architectures are proposed for image classification, namely, actual image feature-based classifier (AIFC), segmented image feature-based classifier (SIFC), actual and segmented image feature-based classifier (ASIFC), actual image-based classifier (AIC), segmented image-based classifier (<italic>SIC</italic>), actual and segmented image-based classifier (ASIC), and finally, CFIC. The Kaggle Brain Tumor Detection 2020 dataset has been used to train and test the proposed classifiers. Among the various classifiers proposed, the CFIC performs better than all other proposed methods. The proposed CFIC method gives significantly better results in terms of sensitivity, specificity, and accuracy with 98.86, 97.14, and 98.97%, respectively, compared with the existing classification methods.</p>