Advancing Brain Tumor Diagnosis through Machine Learning: A Comparative Study
DOI:
https://doi.org/10.47611/jsrhs.v13i3.7733Keywords:
Brain Tumor, Machine Learning, Convolutional Neural Network, Neurology, Algorithm, Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, Random Forest (RF), MRI ImagesAbstract
Brain tumor is a devastating disease affecting thousands of Americans every year. The disease requires an early and accurate diagnosis. Machine learning could be a very powerful way to speed up the diagnosis. This study explores the efficacy of various machine learning models in diagnosing and classifying brain tumors using MRI scans. Convolutional Neural Network (CNN) models were compared with traditional machine learning algorithms, including Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, and Random Forest (RF), on a dataset containing MRI images of different brain tumor types. The study came to the conclusion that the CNN was more effective than other models, and all of these models would need larger datasets before considering them usable as a medical tool.
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