Modified Visual Geometric Group Architecture for MRI Brain Image Classification

N Veni,J Manjula

doi:10.32604/csse.2022.022318

Abstract

The advancement of automated medical diagnosis in biomedical engineering has become an important area of research. Image classification is one of the diagnostic approaches that do not require segmentation which can draw quicker inferences. The proposed non-invasive diagnostic support system in this study is considered as an image classification system where the given brain image is classified as normal or abnormal. The ability of deep learning allows a single model for feature extraction as well as classification whereas the rational models require separate models. One of the best models for image localization and classification is the Visual Geometric Group (VGG) model. In this study, an efficient modified VGG architecture for brain image classification is developed using transfer learning. The pooling layer is modified to enhance the classification capability of VGG architecture. Results show that the modified VGG architecture outperforms the conventional VGG architecture with a 5% improvement in classification accuracy using 16 layers on MRI images of the REpository of Molecular BRAin Neoplasia DaTa (REMBRANDT) database.

Highlights

IntroductionThe anatomical structures of the brain from MRI give rich information for biomedical research
The ability of deep learning is utilized in this study to design a computer system for image classification
The conventional Visual Geometric Group (VGG) architecture is modified to improve the analysis of medical images

Summary

Introduction

The anatomical structures of the brain from MRI give rich information for biomedical research. Many computer systems are developed in the last two decades to aid the diagnosis of brain cancer from MRI images. The extensive works performed on brain images are discussed here. All approaches fall into two categories: supervised and unsupervised. The former one uses Artificial Neural Network (ANN) [1–3], Support Vector Machine (SVM) [4–5], Naive Bayes (NB), and k-Nearest Neighbour (k-NN) [1,6] and the later one uses Fuzzy C-Means (FCM) [7] and self-organizing map [8]. While comparing the performance of these two types of approaches, supervised classification is superior to unsupervised approaches in terms of classification accuracy, as the unsupervised approaches require experts with strong knowledge to select the meaningful features and prone to error for the classification of large scale data

Objectives

Results

Conclusion