Version 1
: Received: 27 June 2020 / Approved: 29 June 2020 / Online: 29 June 2020 (07:27:38 CEST)
How to cite:
Dutta, S.; Bandyopadhyay, S. Cross-Validated AdaBoost Classifier Used for Brain Tumor Detection. Preprints2020, 2020060351. https://doi.org/10.20944/preprints202006.0351.v1
Dutta, S.; Bandyopadhyay, S. Cross-Validated AdaBoost Classifier Used for Brain Tumor Detection. Preprints 2020, 2020060351. https://doi.org/10.20944/preprints202006.0351.v1
Dutta, S.; Bandyopadhyay, S. Cross-Validated AdaBoost Classifier Used for Brain Tumor Detection. Preprints2020, 2020060351. https://doi.org/10.20944/preprints202006.0351.v1
APA Style
Dutta, S., & Bandyopadhyay, S. (2020). Cross-Validated AdaBoost Classifier Used for Brain Tumor Detection. Preprints. https://doi.org/10.20944/preprints202006.0351.v1
Chicago/Turabian Style
Dutta, S. and Samir Bandyopadhyay. 2020 "Cross-Validated AdaBoost Classifier Used for Brain Tumor Detection" Preprints. https://doi.org/10.20944/preprints202006.0351.v1
Abstract
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.
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
