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=Volume 13 - 2022

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=Brain tumor classification acting as a niche role in medical prognosis and effective treatment process. In this work, we have proposed a combined feature and image-based classifier (CFIC) for brain tumor image classification. The various Deep Neural Network 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 (SIC), Actual and Segmented Image based Classifier (ASIC) and finally, Combined Feature and Image based Classifier (CFIC). For the purpose of training and testing of proposed classifiers, the Kaggle’s Brain Tumor Detection 2020 dataset has been used. Among the various classifiers proposed, the CFIC classifier perform better when compared to all other proposed methods. The proposed CFIC method gives significantly better results in terms of sensitivity, specificity and accuracy with scores of 98.86%, 97.14% and 98.97% respectively when compared with existing classification methods.