H.5. Image Processing and Computer Vision
Amirhossein Zare Kordkheili; Amirreza Zare Kordkheili; Sekine Asadi Amiri
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) ...
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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.
H.6. Pattern Recognition
Samira Mavaddati
Abstract
Brain tumors are among the most life-threatening neurological conditions, requiring precise and early diagnosis for effective treatment planning. Traditional deep learning models, such as Convolutional Neural Networks (CNNs) and ResNet-based architectures, have demonstrated promising results in brain ...
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Brain tumors are among the most life-threatening neurological conditions, requiring precise and early diagnosis for effective treatment planning. Traditional deep learning models, such as Convolutional Neural Networks (CNNs) and ResNet-based architectures, have demonstrated promising results in brain tumor classification. However, these models often struggle to capture long-range dependencies within MRI images, which are crucial for accurate classification. To overcome this limitation, we propose a Hybrid CNN-ViT model, combining the strengths of Vision Transformers (ViT) and CNNs to achieve high-precision brain tumor classification. The CNN component effectively extracts local spatial features, while the ViT module captures global contextual relationships within MRI scans. The model is evaluated on a four-class dataset of Glioma, Meningioma, Pituitary tumors, and non-tumor images, achieving an impressive accuracy of 98.37%, surpassing conventional CNN-based methods. By leveraging transfer learning, the approach enhances classification performance while reducing reliance on large-scale labeled datasets. The proposed Hybrid CNN-ViT model offers a scalable, robust, and efficient solution for real-world neuro-oncological diagnostics, significantly improving the accuracy of MRI-based brain tumor detection.
