Deep learning techniques for efficient brain cancer identification

Abstract

Magnetic Resonance Imaging (MRI) is a non-invasive medical imaging technique, which is widely used for the diagnosis and treatment of various neurological disorders, including brain tumors. Accurate segmentation of brain tumors in MRI images is essential for effective diagnosis and treatment planning. Deep learning algorithms have shown significant potential in improving the speed and accuracy of medical image analysis, while reducing human error. In this paper, a deep learning-based approach called Deep Networks with Stochastic Depth (DNSD) is proposed for brain tumor segmentation in MRI images. It leverages the advantages of deep convolutional neural networks, combined with stochastic depth training to enhance feature learning and reduce training complexity. The performance of the proposed technique is evaluated using a publicly available dataset and compared with state-of-the-art deep learning models, such as U-Net and ResNet. Experimental results demonstrate that the proposed method achieves improved segmentation accuracy and computational efficiency compared to existing techniques.