Semi-Supervised Ensemble Classifier with Improved Sparrow Search Algorithm and Its Application in Pulmonary Nodule Detection

The Adaptive Boosting (AdaBoost) classifier is a widely used ensemble learning framework, and it can get good classification results on general datasets. However, challenging to apply the AdaBoost directly pulmonary nodule detection of labeled unlabeled lung CT images since there are still some drawbacks method. Therefore, solve data problem, semi-supervised using an improved sparrow search algorithm (AdaBoost-ISSA-S4VM) was established. Firstly, construct strong several weak classifiers S4VM (AdaBoost-S4VM). Next, in order accuracy problem AdaBoost-S4VM, (SSA) introduced S4VM. Then, sine cosine new labor cooperation structure adopted increase global optimal solution convergence performance algorithm, respectively. Furthermore, based adaptive boosting classifier, AdaBoost-S4VM improved. Finally, effective AdaBoost-ISSA-S4VM model developed for actual publicly available LIDC-IDRI database. experimental have proved that established has images.