  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.
