Deep Feature Extraction with Cubic-SVM for Classification of Brain Tumor


  • Mohammed Bourennane
  • Hilal Naimi
  • Elbar Mohamed



brain tumors, deep learning, Efficientnetb0, VGG-19, Cubic-SVM


Brain tumors (BT) are fatal and debilitating conditions that shorten the typical lifespan of patients. Patients with BTs who receive inadequate treatment and an incorrect diagnosis have a lower chance of survival. Magnetic resonance imaging (MRI) is often employed to assess the tumor. However, because of the massive quantity of data provided by MRI, early BT detection is a complex and time-consuming procedure in biomedical imaging. As a consequence, an automated and efficient strategy is required. The detection of brain tumors or malignancies has been done using a variety of conventional machine learning (ML) approaches. The manually collected properties, however, provide the main problem with these models. The constraints previously stated are addressed by the fusion deep learning model for binary classification of BTs that is presented in this study. The recommended method combines two different CNN (Efficientnetb0, VGG-19) models that automatically extract features and make use of the feature’s classification using a Cubic SVM classifier model. Additionally, the recommended approach displayed outstanding performance in various classification measures, including Accuracy (99.78%), Precision (99.78%), Recall (99.78%), and F1-Score (99.78%), on the same Kaggle (Br35H) dataset. The proposed strategy performs better than current approaches for classifying BTs from MRI images.


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How to Cite

Bourennane, M., Naimi, H., & Mohamed, E. (2024). Deep Feature Extraction with Cubic-SVM for Classification of Brain Tumor. STUDIES IN ENGINEERING AND EXACT SCIENCES, 5(1), 19–35.