Analysis of depth variation of U-NET architecture for brain tumor segmentation

U-NET is a fully convolutional network (FCN) architecture designed to research the segmentation of biomedical images. The depth of the U-NET is one of the major constraints of this model while computing the performances. The larger depth of the U-NET means that its computational complexity is high as well. In certain cases, this large depth, as in the original model, is not justified for biomedical imaging modalities. In this paper, we have done an efficient analysis of U-NET architecture’s depth variation, i.e., after removing different layers. For the analysis, the datasets BraTS-2017 and BraTS-2019, which consist of High-Grade Glioma (HGG) and Low-Grade Glioma (LGG) MR Scans, have been used for tumor segmentation. We have achieved a dice coefficient of at least 0.8866 and as high as 0.8887 on the discovery cohort, and at least 0.8895 and as high as 0.8911 cross-validation replication cohort. The results show that there are the least significant changes occurring in the performance parameters while moving from the higher to the lower depth of the model. Hence, in this paper, we presented that the large depth of U-NET, which costs more in terms of computational complexity, is not always required. Moreover, the U-NET models with depth reduction, which decreases the computational complexity, can achieve nearly the same results as in the case of the original U-NET.