RobU-Net: a heuristic robust multi-class brain tumor segmentation approaches for MRI scans

Abstract

A tumor is an abnormal growth of cells, either cancerous or benign, that develops in an organ. Early detection and segmentation of brain tumors are crucial for effective treatment, but manual analysis by experts is a labor-intensive and time-consuming process. The proposed solution is a deep learning framework called RobU-Net, a modified U-Net, to improve the accuracy and robustness of MRI image segmentation, specifically designed to handle Rician noise in MRI scans. The study uses various encoder-decoder architectures to segment MRI scans, achieving optimal results that match the ground truth. The approach also employs discrete wavelet transforms for contrast enhancement and tests the framework's robustness. This study used the MRI brain tumor dataset with 3064 slices. The space and time analysis have been carried out using sensitivity (Sn), dice loss (DL), dice coefficient (DC), and Jaccard index (JI). A comprehensive assessment of our proposed framework depicted that it is better than recently existing frameworks. We have found the so far highest Sn, DC, and JI values for noise-impregnated and original MRI datasets using two novice architectures (overall best for A1-E2 and optimum or noisy dataset for A2-E1) as (0.9831, 0.9726, and 0.9468) and (0.9199, 0.9781, and 0.9571).

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