Automated brain MRI image classification through machine learning and deep learning approaches
Abstract
Tumors within the human brain cause severe effects on the cerebral system. Therefore, the early detection of such abnormalities needs to be accurately performed, and classifications of such cells also play a pivotal role in planning further treatment procedures. Timely detection and classification of brain tumors remain challenging, motivating the investigation of machine learning and deep learning approaches for computer-assisted image analysis. To serve the purpose, Support Vector Machine, Logistic Regression, Random Forest, U-Net, and k-Nearest Neighbors models for the identification and correct categorization of the brain tumors have been considered. The models have been trained and tested using the brain MRI images. The considered models are implemented in Python, and the outcomes of the models are observed critically to select the best model for the computer-aided diagnosis system. Among the models, Random Forest and k-Nearest Neighbors have illustrated the highest classification accuracy and computational efficiency of 94.44% and 93.89%, with runtimes of 0.073s and 10.97s, respectively, in correct identification and categorization of the brain tumor through MRI images.
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BibTeX
@article{kamarujjaman2026automated,
title = {{Automated brain MRI image classification through machine learning and deep learning approaches}},
author = {Kamarujjaman and Srirupa Das and Diyasha Majumdar and Jadav Chandra Das and Sahabul Alam and Arunangshu Pal},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-73865-1},
url = {https://www.nature.com/articles/s41598-026-73865-1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Automated brain MRI image classification through machine learning and deep learning approaches
AU - Kamarujjaman
AU - Srirupa Das
AU - Diyasha Majumdar
AU - Jadav Chandra Das
AU - Sahabul Alam
AU - Arunangshu Pal
PY - 2026
DA - 2026/10/04
JO - Scientific Reports
DO - 10.1038/s41598-026-73865-1
UR - https://www.nature.com/articles/s41598-026-73865-1
AB - Tumors within the human brain cause severe effects on the cerebral system. Therefore, the early detection of such abnormalities needs to be accurately performed, and classifications of such cells also play a pivotal role in planning further treatment procedures. Timely detection and classification of brain tumors remain challenging, motivating the investigation of machine learning and deep learning approaches for computer-assisted image analysis. To serve the purpose, Support Vector Machine, Logistic Regression, Random Forest, U-Net, and k-Nearest Neighbors models for the identification and correct categorization of the brain tumors have been considered. The models have been trained and tested using the brain MRI images. The considered models are implemented in Python, and the outcomes of the models are observed critically to select the best model for the computer-aided diagnosis system. Among the models, Random Forest and k-Nearest Neighbors have illustrated the highest classification accuracy and computational efficiency of 94.44% and 93.89%, with runtimes of 0.073s and 10.97s, respectively, in correct identification and categorization of the brain tumor through MRI images.
SN - 2045-2322
ER -