Brain Tumor Detection Using U-Net: A Deep Learning Approach

Brain tumors are among the most dangerous and potentially fatal medical disor- ders. They can be benign or malignant. Patient care and treatment planning depend on a timely and precise diagnosis of these tumors. A widely used image technique that aids in the visualisation of brain structures and makes anomalies like tumors visible is magnetic resonance imaging or MRI. However, radiologists must manually analyse MRI scans, which takes time, is labour-intensive, and is prone to human error-especially when the tumors are small or diffuse. Recent developments in the deep learning method known as convolutional neural networks (CNNs), have demonstrated the capability of robotic medical image processing. U-Net is widely regarded as one of the most effective CNN architectures for medical image segmentation; it has been used to good effect in several biomedical image segmentation applications. This paper presents an automated approach for detecting and segmenting brain tumors in MRI scans using a deep learning frame- work based on the U-Net model. To achieve high accuracy and efficient processing, we apply a U-Net architecture to segment tumors from MRI images using the Brain Tumor Segmentation dataset (BraTS). The MRI scans and their related ground-truth masks are employed in the model's training and accuracy and loss are used to assess its performance. Our findings show that U-Net is capable of precisely segmenting brain tumors, which could make it a useful tool for radiologists to use in tumor diagnosis and localisation. With additional improvements, this technique may greatly improve the accuracy and efficacy of tumor diagnosis in clinical settings.