Segmentation and Detection of Brain Tumors using EfficientNetB3 Model

The EfficientNetB3 model has shown to be a significant advancement in the area of medical image analysis, namely in the identification and segmentation of brain tumors. The results of experiments using MRI images for tumor identification using different machine learning models are presented in this research. Conventional methods with a top accuracy of 91.65% were accomplished, including RBF, Linear, and Polygonal kernels. Nevertheless, the EfficientNetB3 model outperformed the others, with an astounding accuracy rate of more than 98%. This noteworthy accomplishment highlights the model’s remarkable capacity to handle all of the challenges associated with brain tumor identification. Because of its efficacy, there are increased opportunities for its use in clinical settings, which might transform early diagnosis and lead to more individualized treatment plans.