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

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

doi:10.3389/fpsyg.2022.848784

Abstract

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 (SIC), 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.

Highlights

The general health of the people and livestock and nature, in general, is considered the foremost wealth component of any nation
The combined feature and image-based classifier (CFIC) performs significantly better than the existing classification methods
The implication is that the border information is last in a segmented image

Summary

Introduction

The general health of the people and livestock and nature, in general, is considered the foremost wealth component of any nation. Improving health and controlling diseases are crucial factors in the sustenance and progress of the world. Identification of diseases is crucial in disease control. The rapid and accurate diagnosis is of foremost significance. There has been a steady growth in the medical instrumentation field in the past and present centuries. With the advent of computers, accurate interpretation of data analysis and measurements has led to vast improvement. Computer-aided analytical tools have become a great help to medical experts in decision-making. Computer-aided diagnosis is a fast-growing research area. Medical image processing (MIP) is one of the important techniques in diagnosis, where classification is a very important process to classify the disease, whether benign or malignant

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Conclusion