Enhancing Brain Tumor Diagnosis Through Hybrid Deep Neural Network Ensemble: Combining Probabilistic Predictions from ResNet50 and DenseNet201

This paper presents a comprehensive comparative ponder of brain tumor classification models, centering on four major categories of brain tumors: glioma, meningioma, pituitary tumor, and ordinary tissues. Leveraging an assortment of conventional and profound learning approaches, they think about points to address one of the foremost critical challenges in restorative picture handling. Manual classification strategies regularly lead to off base analysis and estimate due to human blunder and the complexity of the assignment, especially when managing with significant sums of information. Additionally, the closeness between typical and tumor tissues includes the trouble of precisely recognizing brain tumor districts from MRI checks. To overcome these challenges, we propose a crossover profound learning method for identifying brain tumors from 2D attractive reverberation pictures. The strategy coordinating conventional classification strategies with profound learning models to improve exactness and effectiveness. This approach points to streamline the demonstrative handle, diminishing the manual exertion required and guaranteeing solid expectations, with the extreme objective of encouraging its application in clinical settings.