Deep Learning Hybrid Techniques for Brain Tumor Segmentation

Medical images play an important role in medical diagnosis and treatment. Oncologists analyze images to determine the different characteristics of deadly diseases, plan the therapy, and observe the evolution of the disease. The objective of this paper is to propose a method for the detection of brain tumors. Brain tumors are identified from Magnetic Resonance (MR) images by performing suitable segmentation procedures. The latest technical literature concerning radiographic images of the brain shows that deep learning methods can be implemented to extract specific features of brain tumors, aiding clinical diagnosis. For this reason, most data scientists and AI researchers work on Machine Learning methods for designing automatic screening procedures. Indeed, an automated method would result in quicker segmentation findings, providing a robust output with respect to possible differences in data sources, mostly due to different procedures in data recording and storing, resulting in a more consistent identification of brain tumors. To improve the performance of the segmentation procedure, new architectures are proposed and tested in this paper. We propose deep neural networks for the detection of brain tumors, trained on the MRI scans of patients’ brains. The proposed architectures are based on convolutional neural networks and inception modules for brain tumor segmentation. A comparison of these proposed architectures with the baseline reference ones shows very interesting results. MI-Unet showed a performance increase in comparison to baseline Unet architecture by (Formula presented.) in dice score, (Formula presented.) insensitivity, and (Formula presented.) in specificity. Depth-wise separable MI-Unet showed a performance increase by (Formula presented.) in dice score, (Formula presented.) in sensitivity, and (Formula presented.) in specificity as compared to the baseline Unet architecture. Hybrid Unet architecture achieved performance improvement of (Formula presented.) in dice score, (Formula presented.) in sensitivity, and (Formula presented.) in specificity. Whereas the depth-wise separable hybrid Unet architecture outperformed the baseline architecture by (Formula presented.) in dice score, (Formula presented.) in sensitivity, and (Formula presented.) in specificity.