Deep Learning Based Multi-Scale Approach for Precision Medicine and Quantitative Imaging in Glioblastoma

Motivation: Glioblastoma (GBM) is a fast-growing invasive brain tumor that presents unique treatment challenges. Early diagnosis requires manual segmentation using MRI and histopathological image analysis. Goal(s): Our proposed model can facilitate medical personnel in an efficient and accurate diagnosis of glioblastoma. Approach: We present a multiscale multilevel approach based on deep learning for precision medicine and quantitative imaging in GBM capturing image feature and providing wide-ranging contextual information. Results: Our method predicted the overall survival of GMB patients with an average accuracy of 88.63% and 91.7% DSC (Unet: 84% DSC; Swin Transformer: 87% DSC) on BraTS 2020. Impact: Our model surpasses state-of-the-art methods in Glioblastoma (GBM) segmentation and predicts patient survival with 88.63% accuracy. This research work assists in precise and efficient diagnoses of GBM, potentially contributing to early disease detection and treatment strategies.