Optimized activation for quantum-inspired self-supervised neural network based fully automated brain lesion segmentation

Due to the lack of appropriate tailoring of the inter-connection weights, the segmentation performance of the recently suggested Quantum-inspired Self-supervised Neural Network models suffers from the slow convergence problem. As a result, using quantum-inspired meta-heuristics in Quantum-Inspired Self-supervised Neural Network models improves their hyper-parameters and inter-connection weights. The goal of this paper is to propose an improved version of a Quantum-Inspired Self-supervised Neural Network (QIS-Net) model for brain lesion segmentation. The proposed Optimized Quantum-Inspired Self-supervised Neural Network (Opti-QISNet) model is based on the QIS-Net architecture, and its operations are used to get the best segmentation results. A Quantum-Inspired Optimized Multi-Level Sigmoidal (Opti-QSig) activation is the optimized activation function used in the described model. Three quantum-inspired meta-heuristics improve the Opti-QSig activation function, with fitness evaluated using Otsu’s multi-level thresholding. Experiments were carried out using brain MR images from the Cancer Imaging Archive (TCIA) in the Nature data repository. The results show that the proposed self-supervised Opti-QISNet model outperforms our recently established QIBDS Net and QIS-Net models in brain lesion segmentation, and it is a potential candidate to extensively supervised neural network based architectures (UNet and FCNNs).