Adaptive Multiparticle Swarm Neural Architecture Search for High-Incidence Cancer Prediction

Abstract

Cancer is a disease caused by uncontrolled growth and spread of cells, and early diagnosis is essential to improve the cure rate and reduce mortality. Although machine learning and deep learning have shown great potential in early cancer prediction, the accuracy of detection and prediction still needs to be improved due to the different scales of lesion areas. Therefore, we propose an adaptive multiparticle swarm neural architecture search method to automatically explore an efficient deep neural network architecture for high-incidence cancer prediction. First, the multiparticle swarm strategy is used to initialize the high-quality architecture in the scale adaptive search space to enhance multiscale perception. Then, the improved weighted average method is combined with classification accuracy, parameters, and floating-point operations to adaptively update the particle swarm architecture to avoid falling into local optimum. In addition, a method based on weight sharing is used to improve the efficiency of architecture search. The experimental results show that comparing with the manual design network and the existing neural architecture search method, the proposed algorithm achieves average increments of 26.33%, 33.99%, 8.98%, 37.41%, 35.1%, and 51.76% in classification accuracy, F1-Score, Cohen's kappa, AUC, exponential balance accuracy and search efficiency, respectively.