MRI Intracranial Neoplasm Localization Using Convolution Neural Network Based Residual Block ResUnet: A Systematic Approach

Abstract

Analysis of intracranial neoplasm using multimodal MR images requires accurate and automatic segmentation. However, manually classifying tumors with similar structures or appearances in magnetic resonance imaging (MRI) with similar anatomy or appearances is more challenging, requiring experience to detect brain tumors. Precise segmentation of brain tumors gives clinicians with a foundation for surgical planning and treatment. Due to its capacity to segment brain tumor images automatically, Deep Neural Networks (DNN) have been widely used in image segmentation applications. To classify, segment and marking the occurrence of the brain tumor area accurately, we present custom Deep Convolution Neural Network (CNN) based Residual block U-Net (RB-ResUnet) architecture. Our technique is tested on publicly available Kaggle datasets utilizing quantitative metrics. Comparative results demonstrate that the custom CNN-based RB-ResUnet model can more reliably identify tumor locations and give accurate segmentation masks to tumor locations that are defined by bounding boxes. The findings of the experiment reveal that our proposed model RB-ResUnet can effectively aid in the identification, toxicological evaluation of the brain tumor and has clinical research as well as practical application.