A feature extraction method for lung nodules based on a multichannel principal component analysis network (PCANet)

Abstract

Feature extraction of lung nodules is very important in the diagnosis of lung cancer and is the premise of feature description, target matching, recognition and benign and malignant diagnosis. The main contribution of this work is the development of a new end-to-end feature extraction method that learns effective feature representation from images to effectively establish the direct relationship between multiple features and tissue features (that is, benign or malignant). The architecture consists of three seamlessly connected functional layers. RGB multichannel can automatically extract ROI sequence images involving lung parenchyma from the lung imaging sequence. The feature extraction layers, using Principal Component Analysis Network - random binary hash (PCANet-RBH), a) extract high-level semantic features of the R/G/B channel by cascading PCA and fuse the extracted normal color patterns, and b) generate multiple binary patterns via RBH to produce richer features with color information. The connected spatial pyramid pooling (SPP) layer can extract the location features of the lung nodules and map the feature matrix to the low-dimensional space and then establish a correspondence between the image features and the organizational identity. We validate the performance of the proposed method using the public dataset LIDC. The experimental results show that the fusion features extracted by our method have high and stable classification accuracy (accuracy:93.25 ± 0.53, sensitivity:93.12 ± 0.62 specificity:91.37 ± 0.62), which is significantly better than the traditional algorithm for lung nodule feature extraction. Moreover, RGB-PCANet has a short training time, which can meet the requirements of real-time diagnosis of lung cancer. In general, the advantage of our framework is that it provides a better and more comprehensive method to establish a direct relationship between image high-level semantic features, color features, location features and tissue features, making it an attractive clinical diagnostic tool for lung cancer.