Hyber Selective Ensemble Methodology Based on Deep Transfer Learning For Brain Diagnosis Detection

Authors

  • Amal fouad Abd El-Hady Department of Multimedia, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef City, 62511, Egypt
  • Hesham Ahmed Hefny Department of Computer Sciences, Faculty of Graduate Studies for Statistical Research, Cairo University, Cairo, Egypt
  • Rowayda Abd El-hamid Sadek Department of Computer Sciences, Faculty of Computers and Artificial Intelligence, Helwan University, Helwan, Egypt
  • Hossam M. Moftah Department of Multimedia, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef City, 62511, Egypt

Keywords:

CNN Architectures Ensemble Learning, Transfer learning, Machine Learning, Artificial Neural Network, Convolutional Neural Network, Deep residual Network Cancer Classification.

Abstract

Machine Learning models initiate to have a great effect on the diagnosis of numerous diseases. In the

biomedical field, Convolutional neural networks (CNNs) display a potential role for computer-aided diagnosis (CAD) by

extracting features directly from the image data instead of the features based on analytically methods or handcrafts

features. However, CNNs have many challenges to train medical images from scratch as small sample sizes and variations

in tumor presentations. Additionally, it needs more hardware for processing. Alternatively, transfer learning can extract

from medical images tumor information through CNNs originally pre-trained for nonmedical images, which cover the

shortage of a small dataset.

The proposed model introduces several pre-trained models such as Xception, VGG16, VGG19, ResNet50, MobileNet,

MobileNetV2, and InceptionResNetV2 to create a selective ensemble model from them which achieves 97.77accuracy on

brain tumor type classification.

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Published

2026-01-23

Issue

Vol. 11 No. 01 (2022): 2022 (Continuous)

Section

Articles