TY - GEN
T1 - Machine Learning Methods for Multiclass Brain Tumor Classification in MRI Scans
T2 - International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2023
AU - Aradan, John F.
AU - Pawar, Arti
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - The brain is one of the most vital organs, and yet, it has become the second highest reason of death around the globe. The reasons for which being misdiagnosis, lack of physicians with the required expertise, and delay in decision-making. These problems can be easily resolved using machine learning approaches. In this paper, the performance of various machine learning algorithms is compared to identify which model can give the best performance to perform a multiclass classification of brain MRI scans. For this study, we used a dataset containing three types of tumors present in the brain. The performance of six machine learning algorithms is compared: SVM, decision tree, random forest, Naive Bayes, logistic regression, and neural networks. The performance of each of these metrics was later evaluated on the accuracy metric.
AB - The brain is one of the most vital organs, and yet, it has become the second highest reason of death around the globe. The reasons for which being misdiagnosis, lack of physicians with the required expertise, and delay in decision-making. These problems can be easily resolved using machine learning approaches. In this paper, the performance of various machine learning algorithms is compared to identify which model can give the best performance to perform a multiclass classification of brain MRI scans. For this study, we used a dataset containing three types of tumors present in the brain. The performance of six machine learning algorithms is compared: SVM, decision tree, random forest, Naive Bayes, logistic regression, and neural networks. The performance of each of these metrics was later evaluated on the accuracy metric.
UR - https://www.scopus.com/pages/publications/85208060556
UR - https://www.scopus.com/pages/publications/85208060556#tab=citedBy
U2 - 10.1007/978-981-97-4650-7_45
DO - 10.1007/978-981-97-4650-7_45
M3 - Conference contribution
AN - SCOPUS:85208060556
SN - 9789819746491
T3 - Lecture Notes in Electrical Engineering
SP - 605
EP - 614
BT - Intelligent Control, Robotics, and Industrial Automation - Proceedings of International Conference, RCAAI 2023
A2 - Suresh, Shilpa
A2 - Lal, Shyam
A2 - Kiran, Mustafa Servet
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 12 October 2023 through 14 October 2023
ER -