Segmentation Of Brain Tumor Regions In Magnetic Resonance Imaging Using Convolutional Neural Networks

Abstract

This paper proposes a novel method for the segmentation of brain tumor regions from magnetic resonance imaging (MRI) scans using Convolutional Neural Networks (CNNs). The increasing prevalence of brain tumors and the criticality of their accurate diagnosis
necessitate the development of advanced, automated techniques. Traditional methods for MRI brain tumor segmentation often suffer from inaccuracy and subjectivity due to the complex nature of the tumor's irregular shapes, diverse sizes, and varying locations. To
address these challenges, this study develops and evaluates a CNN-based method that can learn complex features of brain tumors, thereby improving the accuracy of segmentation. Our proposed model employs a deep learning approach to automatically extract features from MRI scans and differentiate between healthy and tumor tissues. It utilizes multimodal MRI scans to maximize the capture of tumor characteristics. The model is designed to be robust against noise and variability in tumor appearances and positions, and it significantly outperforms traditional methods and some state-of-the-art deep learning models in terms of precision, recall, and Dice coefficient. Furthermore, our model shows excellent generalizability when tested on unseen data, demonstrating its potential for
real-world clinical applications. This research opens the door for more accurate, timely, and objective diagnosis of brain tumors, and it shows promise for further applications of CNNs in medical imaging. Future work will aim to improve upon this model by incorporating additional clinical parameters and exploring other deep learning architectures.