Brain Tumor Detection using Machine Learning Methods-KNN, SVM, DT, RF

Muskan Singh, Parishi Ujwal Bhange, Mr Jamkhongam Touthang

Abstract


Brain tumor is a major contributor to illness and death worldwide, and timely diagnosis is essential for successful treatment and improved outcomes Recent research has looked into the possibility of machine learning algorithms for detecting and classifying brain tumours in medical pictures. In this study, four commonly used machine learning algorithms (K-Nearest Neighbours, Decision Trees, Support Vector Machines, and Random Forests) were evaluated for their effectiveness in brain tumor detection. The study utilized a dataset of MRI brain scans, and applied preprocessing techniques to normalize pixel intensity and extract relevant features from the images. The models' performance was assessed using accuracy, precision, recall, F1-score, and F2-score, with the Random Forest algorithm achieving the greatest accuracy (87.4%) and precision (89.4%). The results of this study suggest that machine learning techniques can be a valuable tool in the accurate and efficient detection of brain tumors.

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https://www. kaggle. com/datasets/sartajbhuvaji/braintumor-classification- mri?resource=download




DOI: https://doi.org/10.37628/jcep.v9i2.1427

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