Akademik Veri Yönetim Sistemi
Expert Systems with Applications, cilt.268, 2025 (SCI-Expanded)
Accurate grading of gliomas and classification of various brain tumor types are essential for diagnosing and treating brain cancer. In this work, a new combined network structure has been designed and implemented to effectively capture detailed and global feature information for precise pattern recognition and classification. It utilizes the advantages of both transformers and CNNs. The structure is constructed using five blocks that have been carefully designed and properly located to achieve high-accuracy classifications. Among these blocks, there exist not only blocks designed homogeneously, including only CNNs or transformers, but also heterogeneously designed blocks, including an effective combination of both CNNs and transformers. Also, for fair comparative evaluations, state-of-the-art brain tumor classifications have been applied to the same datasets, and their capabilities have been evaluated using the same assessment criteria. Therefore, the main contributions of this paper are fourfold: (i) Introducing a novel network utilizing the advantages of both transformers and CNN structures. (ii) Demonstrating the effectiveness of the network in pattern recognition for glioma grading and multi-class classifications of brain tumors. (iii) Providing fair comparative assessments of recent methods, trained and tested with the same datasets and evaluated using the same metrics. (iv) Presenting experimental results demonstrating that the proposed approach achieves higher accuracy in both glioma grading (99.21%) and brain tumor classification (98.66%) compared to recent methods.