Brain Tumor Classification based on Improved Stacked Ensemble Deep Learning Methods

Al-Azzwi, Zobeda Hatif Naji; Nazarov, A.N

doi:10.31557/APJCP.2023.24.6.2141

Brain Tumor Classification based on Improved Stacked Ensemble Deep Learning Methods

Document Type : Research Articles

Authors

¹ School of Radio Engineering and Computer Technology, Moscow Institute of Physics and Technology, Moscow, Russian Federation.

² Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Moscow, Russian Federation.

10.31557/APJCP.2023.24.6.2141

Abstract

Objective: Brain Tumor diagnostic prediction is essential for assisting radiologists and other healthcare professionals in identifying and classifying brain tumors. For the diagnosis and treatment of cancer diseases, prediction and classification accuracy are crucial. The aim of this study was to improve ensemble deep learning models for classifing brain tumor and increase the performance of structure models by combining different model of deep learning to develop a model with more accurate predictions than the individual models. Methods: Convolutional neural networks (CNNs), which are made up of a single algorithm called CNN model, are the foundation of most current methods for classifying cancer illness images. The model CNN is combined with other models to create other methods of classification called ensemble method. However, compared to a single machine learning algorithm, ensemble machine learning models are more accurate. This study used stacked ensemble deep learning technology. The data set used in this study was obtained from Kaggle and included two categories: abnormal & normal brains. The data set was trained with three models: VGG19, Inception v3, and Resnet 10. Result: The 96.6% accuracy for binary classification (0,1) have been achieved by stacked ensemble deep learning model with Loss binary cross entropy, and Adam optimizer take into consideration with stacking models. Conclusion: The stacked ensemble deep learning model can be improved over a single framework.

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