Machine Learning Assisted Methodology for Multiclass Classification of Malignant Brain Tumors

Ankit Vidyarthi, Ruchi Agarwal, Deepak Gupta, Rahul Sharma, Dirk Draheim, Prayag Tiwari*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

24 Citations (Scopus)
59 Downloads (Pure)


Analysis of malignant and non-malignant brain tumors is done using a computer-aided diagnosis system by practitioners worldwide. Radiologists refer computer-assisted techniques to draw conclusions using image modalities and inferences. Pedagogically, various machine learning approaches have been used, which usually focus on the classification of imaging modality into two categories, either normal and abnormal images or differentiating between benign and malignant tumors. Still, the work requirement is to classify these multi-class malignant tumors into their specific class with better precision. The proposed work focuses on distinguishing between the types of high-grade malignant brain tumors. This study is performed on real-life malignant brain tumor datasets having five classes. The proposed methodology uses the vast feature set from six domains to capture most of the hidden information in the extracted region of interest. Later, relevant features are extracted from the feature set pool using a new proposed feature selection algorithm named the Cumulative Variance method (CVM). Next, the selected features are used for model training and testing using K-Nearest Neighbour (KNN), multi-class Support Vector Machine (mSVM), and Neural Network (NN) for predicting multi-class classification accuracy. The experiments are performed using the proposed feature selection algorithm with three classifiers. The mean average classification accuracy achieved by using the proposed approach is88.43% (KNN), 92.5% (mSVM), and 95.86% (NN), respectively. The comparative analysis of the proposed approach with other existing algorithms like ICA, and GA suggest that the proposed approach gains an increase of accuracy around 2% (KNN), 3% (SVM), and 4% (NN).The experimentation results concluded that the proposed approach is found better with NN classifier with an accuracy of 95.86% using diversified features.

Original languageEnglish
Pages (from-to)50624-50640
Number of pages17
JournalIEEE Access
Publication statusPublished - 2022
MoE publication typeA1 Journal article-refereed


  • Feature extraction
  • Feature selection
  • K-nearest neighbour (KNN)
  • Malignant brain tumors
  • Multi-class support vector machine (m-SVM)
  • Neural network
  • Region of interests (ROI's)


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