CLASSIFICATION ANALYSIS OF BRAIN TUMOR DISEASE IN RADIOGRAPHIC IMAGES USING SUPPORT VECTOR MACHINES (SVM) WITH PYTHON
DOI:
https://doi.org/10.22437/jop.v9i3.36270Keywords:
brain tumor, radiographic images, Support Vector Machines (SVM), Phython.Abstract
This research examines the analysis of brain tumor disease classification using radiographic images using the Python-based Support Vector Machines (SVM) method. Data was collected from the Kaggle platform with four main categories of brain tumors: normal, pituitary, glioma, and meningioma. The data is then processed, including cleaning, pixel intensity normalization, and feature extraction to distinguish brain tumor characteristics. The data were visualized to understand the distribution and characteristics of the tumor. With the implementation of Python, visual analysis becomes efficient. The SVM model was trained and evaluated, showing an accuracy of 90% with good evaluation metrics such as MAE, MSE, RMSE, and F1-SCORE. The results show that SVM has excellent potential as a diagnostic tool to support the identification and treatment of brain tumors.
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Copyright (c) 2024 Jihan Suci Ananda, Yoza Fendriani, Jesi Pebralia
This work is licensed under a Creative Commons Attribution 4.0 International License.
