| Brain Tumor Classification Using Statistical and Texture Features: An Evaluation of Machine Learning Models |
| Paper ID : 1006-ICEEM2025 (R2) |
| Authors |
|
Marwa M. Eid1, Doaa Sami Khafaga2, Khaled Sh. Gaber3, Mahmoud Elshabrawy Mohamed4, El-Sayed M. El-Kenawy *5 1Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt 2Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia 3Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA 4Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Egypt 5Delta Higher Institute for Engineering and Technology |
| Abstract |
| Classification of brain tumors is still an essential problem in medical imaging, where the differentiation between tumor and non-tumor tissues may enhance the diagnosis and treatment processes. In this paper, the potential of different first-order statistical and texture-based features of tumors in the brain. The dataset comprises five first-order features (mean, variance, standard deviation, skewness, and kurtosis) and eight texture features (e.g., contrast, energy, and entropy) that capture essential characteristics in pixel intensity and spatial relationships within brain scans. These features are harnessed to enhance the differentiation between tumor and non-tumor classification tasks. In our analysis, we compared DecisionTreeClassifier, RandomForestClassifier and GaussionNB. Our results indicated that RF and DT were the best models, with accuracies of about 0.975. The study's findings also show that the statistical and texture features are essential in enhancing classification performance, hence the significance of highly correctly classified instances in detecting brain tumors. However, several of the specified models had limitations, which may imply the need to optimize further to make them effective models for clinical use. Overall, this research has implications for improving the existing brain tumor classification approaches that could be generalized better in actual healthcare facilities. |
| Keywords |
| brain tumor classification, machine learning, first-order statistical features, texture features, Random Forest Classifier, medical imaging |
| Status: Accepted |