🤖 AI Summary
Manual diagnosis of brain tumors from MRI scans is time-consuming and prone to inter-observer variability, necessitating robust automated classification methods. This paper proposes a deep ensemble model based on multi-backbone transfer learning, integrating Xception, ResNet50V2, ResNet152V2, and InceptionResNetV2. To optimize ensemble performance, we introduce two novel dynamic weight allocation strategies: Genetic Algorithm-based Weight Optimization (GAWO) and Grid Search-based Weight Optimization (GSWO). Evaluated on the Figshare CE-MRI dataset (3,064 images), the GSWO ensemble achieves a 5-fold cross-validated average accuracy of 99.76%, substantially outperforming individual models and state-of-the-art approaches. To our knowledge, this is the first work to apply GAWO/GSWO to brain tumor MRI classification—achieving significant improvements in discriminative robustness while maintaining computational feasibility. The method demonstrates strong potential for real-time clinical decision support.
📝 Abstract
Brain tumors present a grave risk to human life, demanding precise and timely diagnosis for effective treatment. Inaccurate identification of brain tumors can significantly diminish life expectancy, underscoring the critical need for precise diagnostic methods. Manual identification of brain tumors within vast Magnetic Resonance Imaging (MRI) image datasets is arduous and time-consuming. Thus, the development of a reliable deep learning (DL) model is essential to enhance diagnostic accuracy and ultimately save lives. This study introduces an innovative optimization-based deep ensemble approach employing transfer learning (TL) to efficiently classify brain tumors. Our methodology includes meticulous preprocessing, reconstruction of TL architectures, fine-tuning, and ensemble DL models utilizing weighted optimization techniques such as Genetic Algorithm-based Weight Optimization (GAWO) and Grid Search-based Weight Optimization (GSWO). Experimentation is conducted on the Figshare Contrast-Enhanced MRI (CE-MRI) brain tumor dataset, comprising 3064 images. Our approach achieves notable accuracy scores, with Xception, ResNet50V2, ResNet152V2, InceptionResNetV2, GAWO, and GSWO attaining 99.42%, 98.37%, 98.22%, 98.26%, 99.71%, and 99.76% accuracy, respectively. Notably, GSWO demonstrates superior accuracy, averaging 99.76% accuracy across five folds on the Figshare CE-MRI brain tumor dataset. The comparative analysis highlights the significant performance enhancement of our proposed model over existing counterparts. In conclusion, our optimized deep ensemble model exhibits exceptional accuracy in swiftly classifying brain tumors. Furthermore, it has the potential to assist neurologists and clinicians in making accurate and immediate diagnostic decisions.
