A Multimodel Accurate Classification of Brain Tumor Based on Deep Learning Techniques

A brain tumor is a highly persistent disease, with over 84,000 people expected to receive a primary brain tumor diagnosis in 2023, resulting in an estimated 24,000 deaths from meningioma tumors in the same year. Magnetic resonance imaging (MRI) is the most effective technique for detecting brain tumors. Neural networks have demonstrated remarkable accuracy in solving image classification and segmentation problems. In this paper, we present a multimodal accurate classification based on neural networks to identify and classify brain tumors into glioma, meningioma, pituitary, or no tumors. Using different optimizer models has been integral to achieving higher accuracy, resulting in MobileNetV2 and MobileNetV3 consequently producing improved results for the multi-class tumor classification model. The proposed model exhibits significant performance improvements and excellent validation accuracy.