MRI Brain Tumor Analysis on Improved VGG-16 and Efficient NetB7 Models

Abstract

In recent years, Magnetic Resonance Imaging (MRI) scanning has been the most rapidly developing field. Concerning the tumor’s size and specifics, diagnosing and classifying brain tumor’s is challenging and time-consuming for radiologists. The growth of abnormal cells in the brain is referred to as a brain tumor. A brain tumor is diagnosed in about 11,700 patients every year. It is estimated that 34% of males and 36% of females will survive five years after being diagnosed with malignant brain or different tumors. This study focuses on meningioma, pituitary, glioma and no tumors’, among the many brain tumors. Deep learning algorithms and machine learning methods were used to create an autonomous classification and segmentation system for brain tumors, significantly improving early detection. Using the Visual Geometry Group (16), parameters were set for training the model that detects brain tumors based on analysis of proposed literature solutions. Simple Convolution Neural Network (CNN) models such as VGG-16 and Efficient NetB7 perform well because they are among the highest-performing models. As a result of the study, quick, efficient, and precise decisions can be made using MRI to detect brain tumors. For this 7022 brain magnetic resonance images were used to train and test this model. According to experimental findings, the suggested differential deep CNN model could accurately categories MRI pictures of brain tumors, including aberrant and standard images, with a 98.19% accuracy rate.