Multi-Model CNN Approaches for Prediction of Brain Tumor Using MRI Images

This research paper focuses on its quickly and accurately diagnosis of brain tumors by using Convolutional Neural Networks (CNN) on MRI images. Because they can have serious consequences, brain tumors are a serious health risk that should be detected as soon as possible. The importance of early diagnosis drives this investigation of cutting-edge technological solutions, especially those that make use of AI and ML. In this work, the research uses the most recent deep learning models i.e. Convolutional Neural Networks (CNN) utilizing Residual Networks (ResNet50), Visual Geometry Group 16 (VGG16) and U – Shaped Neural Network (U-Net) to accurately identify brain tumors, with an emphasis on anticipating their early stages of development. The proposed method aims to improve accuracy and enable early detection by building upon previous research on Magnetic Resonance Imaging (MRI) scans of brain tumor datasets. The suggested approach tends to boost accuracy and facilitate early detection as well by looking into prior MRI studies on brain tumor image datasets. Via integrating AI with medical imaging, this work is an important contribution to the ongoing process of developing an integrated approach to a resolution of the contemporary global health challenge. The recent research on brain tumor detection provides the ray of hope for the future, and it also emphasizes the need for an algorithm approach for better brain tumors management. From the built on a well-defined design VGG16 was the best among all different models, accuracy reached even to 96.6%. The constructive and unmatched accuracy of VGG16 shows the strength of AI and medical imaging, which will make it easier to use different measures to fight this conflict that is spreading all over the world.