Document Type : Original/Review Paper

Authors

• Amirhossein Zare Kordkheili ¹
• Amirreza Zare Kordkheili ²
• Sekine Asadi Amiri ¹

¹ University of Mazandaran

² Department of Computer Engineering, University of Tehran, Tehran, Iran

Abstract

Brain tumor detection is a critical task in medical imaging, requiring accurate and reliable methods. Recent advancements in deep learning have shown great potential in this field. In this article, we present a novel method for brain tumor detection based on a Convolutional Block Attention Module (CBAM) enhanced attention ensemble of deep learning networks. Initially, image augmentation is applied to increase data diversity. We utilize two deep neural network models, EfficientNet-B1 and ResNet-101, for tumor detection. First, we enhance the performance of these models by integrating the CBAM attention module into their architectures. Then, we ensemble the two networks using a soft voting strategy to achieve higher detection accuracy. The proposed method is evaluated on the three-class Figshare dataset, achieving an accuracy of 99.09% in detecting tumors in MRI images, which outperforms existing methods. This approach leverages the strengths of an ensemble of models, offering a promising solution for improving the accuracy and reliability of brain tumor detection in medical imaging.

Keywords

• Brain Tumor Classification
• EfficientNetB1
• ResNet-101
• Transfer learning
• Soft Voting

Main Subjects

• H.5. Image Processing and Computer Vision