AI to automate brain tumor classification using visual geometric group (VGG) 16

Abstract

A brain is the most important part of our body, which authorize the center capacities which attributes inside actual body and predictable with the National mind tumor Society, around 700,000 individuals acknowledge a cerebrum tumor, and in this manner, the figure will ascend to 787,000 by the peak point of 2020. Clinical imaging for medical diagnosis of a variety of disorders has benefited from ongoing advancements in the field of deep understanding. Task CNN is the most common and widely used AI computation for visual learning and image identification. Essentially, we show the Convolutional Neural Network (CNN) strategy, together with Data Augmentation and Image Processing, to sort cerebrum MRI examination images into carcinogenic and non-harmful categories in our hypothesis. A programmable mind tumour layout based on deep learning-based Visual Geometric Group (VGG) 16 is shown in this study. The model (VGG-16) has a 96 percent and 96 percent accuracy for both training and testing photos respectively.