Multi-Level Dimensionality Reduction of Head and Neck Cancer Image Feature Selection Method

Abstract

Aiming at the high dimensional problem of pathological image morphometric features and the small amount of applied samples in medical field, this paper presents the ReliefF-HEPSO head and neck cancer pathological image feature selection algorithm. The algorithm constructs a framework of multi-level dimensionality reduction. Firstly, according to the correlation of features and categories, the ReliefF algorithm is used to determine different feature weights to achieve initial dimensionality reduction. Secondly, the evolutionary neural strategy (ENS) is used to enrich the particle populations diversity of the binary particle swarm optimization (BPSO). A hybrid binary evolutionary particle swarm optimization (HEPSO) algorithm is proposed to automatically search for the best feature subsets of candidate feature subsets. Compared with 7 feature selection algorithms, the experiment proves that the algorithm can effectively screen out the morphological features of high correlation pathology images, achieve rapid dimensionality reduction, and obtain higher classification performance with fewer features.

Keywords

• image features
• small sample high dimension
• relieff algorithm
• evolutionary neural strategy (ens)
• particle swarm optimization (pso)