Segmentation of Medical Images with Adaptable Multifunctional Discretization Bayesian Neural Networks and Gaussian Operations

Abstract

Bayesian statistics is incorporated into a neural network to create a Bayesian neural network (BNN) that adds posterior inference aims at preventing overfitting. BNNs are frequently used in medical image segmentation because they provide a stochastic viewpoint of segmentation approaches by producing a posterior probability with conventional limitations and allowing the depiction of uncertainty over following distributions. However, the actual efficacy of BNNs is constrained by the difficulty in selecting expressive discretization and accepting suitable following disseminations in a higher-order domain. Functional discretization BNN using Gaussian processes (GPs) that analyze medical image segmentation is proposed in this paper. Here, a discretization inference has been assumed in the functional domain by considering the former and dynamic consequent distributions to be GPs. An upsampling operator that utilizes a content-based feature extraction has been proposed. This is an adaptive method for extracting features after feature mapping is used in conjunction with the functional evidence lower bound and weights. This results in a loss-aware segmentation network that achieves an F1-score of 91.54%, accuracy of 90.24%, specificity of 88.54%, and precision of 80.24%.