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Automated Classification and Efficient detection in Brain MRI using SVM and Decision Trees

Publication Type : Journal Article

Publisher : IEEE

Source : 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI)

Url : https://doi.org/10.1109/icdsaai65575.2025.11011569

Campus : Amritapuri

School : School of Engineering

Center : Humanitarian Technology (HuT) Labs

Department : Electronics and Communication

Year : 2025

Abstract : There is a demand to analyze normality or abnormality in MR images, which is a significant task in medical image analysis. Now a days, machine learning methods are applied to identify if the MR image is normal or abnormal. In this work, support vector Machine (SVM) and Decision trees (DT) are applied to MR images to find out abnormalities. Texture features are extracted and analyzed by classification methods such as SVM and DT. The performance parameters on both methods are analyzed, DT provides the classification accuracy of 99% compared to SVM.

Cite this Research Publication : Aruna Devi. B, Rajesh Kannan Megalingam, Rishi Prannav.K, Automated Classification and Efficient detection in Brain MRI using SVM and Decision Trees, 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI), IEEE, 2025, https://doi.org/10.1109/icdsaai65575.2025.11011569

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