BRAIN NEOPLASM IDENTIFICATION USING DEEP LEARNING

Abstract

This study offers a powerful brain tumor detection system that makes use of deep learning techniques, especially the VGG16 learning algorithm and Convolutional Neural Network techniques (CNN). The ultimate objective of the recommended strategy is to enhance the accuracy and efficiency of identifying tumors in magnetic resonance imaging (MRI) images.. Utilizing CNN allows the model to automatically extract intricate features from input images, while VGG16, renowned for its deep architecture, contributes to a more intricate understanding of complex patterns. The integration of these algorithms empowers the system to discern subtle nuances indicative of brain tumors, providing a reliable and swift diagnostic tool for medical practitioners. Experimental results demonstrate the system's effectiveness in achieving high accuracy rates, showcasing its potential as an invaluable asset in early tumor detection and subsequent medical intervention. KEYWORDS: CNN, Brain tumor, Machine learning, Medical imaging