Aliyu Tetengi Ibrahim, Ibrahim Hayatu Hassan, Mohammed Abdullahi, Armand Florentin Donfack Kana, Amina Hassan Abubakar, Mohammed Tukur Mohammed, Lubna A. Gabralla, Mohamad Khoiru Rusydi, Haruna Chiroma
In medical diagnostics, brain tumor classification remains essential, as accurate and efficient models aid medical professionals in early detection and treatment planning. Deep learning methodologies for brain tumor classification have gained popularity due to their potential to deliver prompt and precise diagnostic results. This article proposes a novel classification technique that integrates the Xception model with a hybrid attention mechanism and progressive image resizing to enhance performance. The methodology is built on a combination of preprocessing techniques, transfer learning architecture reconstruction, and dynamic fine-tuning strategies. To optimize key hyper-parameters, this study employed the Dynamic Chaotic Whale Optimization Algorithm. Additionally, we developed a novel learning rate scheduler that dynamically adjusts the learning rate based on image size at each training phase, improving training efficiency and model adaptability. Batch sizes and layer freezing methods were also adjusted according to image size. We constructed an ensemble approach by preserving models trained on different image sizes and merging their results using weighted averaging, bagging, boosting, stacking, blending, and voting techniques. Our proposed method was evaluated on benchmark datasets achieving remarkable accuracies of 99.67%, 99.09%, and 99.67% compared to the classical algorithms. © 2025 by the authors.
Department of Computer Science, Faculty of Physical Sciences, Ahmadu Bello University, Zaria, 810006, Nigeria; National Cereals Research Institute, Badeggi, 912104, Nigeria; Department of Computer Science, Applied College, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia; Faculty of Economics and Business, Universitas Brawijaya, Kota Malang, 65145, Indonesia; College of Computer Science and Engineering, University of Hafr Batin, Hafar Al Batin, 39524, Saudi Arabia
