Deep-Net: Brain Lesion Segmentation with 3D CNN and Residual Connections

The utilization of precise automated algorithms in the segmentation of brain tumors has the potential to enhance disease diagnosis, streamline treatment monitoring, and facilitate comprehensive studies of pathology on a large scale. This innovative approach not only enhances the efficiency of the model but also exhibits significant promise for segmentation tasks, particularly within the realm of medical imaging. Leveraging the residual connections of the initial layer, the proposed convolutional neural network (CNN) produces segmentation results that are more accurate and robust. Its adaptability to diverse datasets and improved generalization capabilities positions it as a valuable asset for in-depth imaging studies. During the testing phase on the Brats 2015 training database, the performance of the proposed method demonstrated excellence compared to alternative architectural configurations. Through a comparative analysis, we illustrate the effectiveness of integrating residual connections into the first layer of our approach. Our approach has undergone testing using the BRATS 2015 training database, demonstrating robust performance despite the simplicity of the pipeline.