Research Article
Volume 12 Issue 8 - 2020
Cross-Validated AdaBoost Classifier Used for Brain Tumor Detection
Shawni Dutta1 and Samir Kumar Bandyopadhyay2*
1Department of Computer Science, The Bhawanipur Education Society College, Kolkata, India
2Professor, Academic Advisor, The Bhawanipur Education Society College, Kolkata, India
*Corresponding Author: Samir Kumar Bandyopadhyay, Professor, Academic Advisor, The Bhawanipur Education Society College, Kolkata, India.
Received: June 27, 2020; Published: July 13, 2020


Brain Tumor is one of the severe diseases and occurrence of this disease threats human life. Detection of brain tumor in advance can secure patient’s life from unwanted loss. Well-timed and swift disease detection and treatment strategy can lead to improved quality of life in these patients. This paper attempts to use Machine Learning based ensemble approaches for recognising patients with brain tumor. Ensemble technique based AdaBoost classifier and 10-fold stratified cross-validation method are assembled in single platform is proposed in this paper for prediction of brain tumor. This prediction is compared against three baseline classifiers such as Gradient Boost, Random Forest and Extra Trees classifier. Experimental result implies the superiority of this model with an accuracy of 98.97%, f1-score of 0.99, kappa statistics score of 0.95 and MSE of 0.0103.

Keywords: Brain Tumor; Machine Learning; Ensemble Techniques; AdaBoost; Cross-Validation; Stratified Technique


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Citation: Shawni Dutta and Samir Kumar Bandyopadhyay. “Cross-Validated AdaBoost Classifier Used for Brain Tumor Detection”. EC Neurology 12.8 (2020): 50-57.

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