Shahrood University of Technology Journal of AI and Data Mining 2322-5211 Articles in Press 2026 06 06 Ensemble of EfficientNet B1 and ResNet 101 with Attention Mechanism for Brain Tumor Classification in MRI Images 3802 10.22044/jadm.2026.17084.2845 EN Amirhossein Zare Kordkheili University of Mazandaran Amirreza Zare Kordkheili Department of Computer Engineering, University of Tehran, Tehran, Iran Sekine Asadi Amiri University of Mazandaran Journal Article 2025 11 04 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.