Brain Tumor Detection Using Convolutional Neural Network

Brain Tumor Detection Using Convolutional Neural  Network

Harsh Raj a, Yugal Sahu a, Yalagiri Swetha Vani a, Yamuna B a, Yatalaboina Siri Praseeda a

a Department of Computer Science and Engineering, Presidency University, Bangalore -560064, India

Abstract:

Brain tumors refer to the abnormal growth of cells in the brain's tissues. Detecting these tumors and accurately determining their size can be challenging when planning treatment. Magnetic resonance imaging (MRI) utilizes strong magnets to produce detailed images of the body's interior, including the brain. Compared to traditional imaging methods, MRI provides clearer visualization of the brain, making it a common approach for identifying brain tumors..

Although human experts can manually detect brain tumors, this approach is prone to human error and time-consuming. Alternatively, artificial neural networks, specifically algorithms based on Convolutional Neural Networks (CNN), can analyze MRI scans and accurately identify brain tumors. The objective of this project is to develop a "Brain Tumor Detection Model" using CNN. This model can automatically detect brain cancers by leveraging CNN classification techniques, as demonstrated in a study referenced.