Multivariate technique for the prediction and classification of brain tumor using deep shallow network

Abstract

Brain tumors, a major cause of death, carry a substantial socio-economic burden. It is essential to distinguish between various types of tumors, such as gliomas, meningiomas, and pituitary tumors, using MRI data. This assists radiologists and eliminates the need for risky histology biopsies. The objective of the proposed work is to create an automated system capable of distinguishing tumor types. This system integrates a deep, shallow network with the ResNet18 model for feature extraction and machine learning methods for classification on images from the Kaggle dataset. The standalone ResNet18 model produced an accuracy of 91.42 % and to enhance the accuracy, features extracted from the pool5 layer of the ResNet18 architecture were examined by chi-square algorithm. The optimal deep features extracted using the chi-square algorithm were integrated with 17 machine learning classifier techniques comprising variants of SVM, KNN, and ensemble techniques. The proposed method predicted the classes, yielding an accuracy of 99.9 % for cubic SVM among the variants of SVM, 94 % for cosine KNN among the variants of KNN, and 99.8 % for the Adaboost algorithm only, with a maximum of the top 10 features justified using local interpretable model-agnostic (LIME) and gradient-weighted class activation mapping (Grad-CAM) visualization techniques.