The present invention discloses an improved deep
Boltzmann machine-based
pulmonary nodule feature extraction and benign and malignant classification method. The method includes the following steps that: step A, pulmonary nodules are segmented from CT images through using a
threshold probability image graph method, so that regions of interest (ROI) are obtained, and the regions of interest are
cut into nodule images of the same size; and step B, a supervised
deep learning algorithm Pnd-EBM is designed to realize the diagnosis of a
pulmonary nodule, wherein the diagnosis of the
pulmonary nodule further includes three major steps: B1, a deep
Boltzmann machine (
DBM) is adopted to extract the features of the ROI of the pulmonary nodule which have deep expression abilities; B2, a sparse cross-entropy
penalty factor is adopted to improve a cost function, so that the phenomenon of feature homogenization in a training process can be avoided; and B3, an
extreme learning machine (ELM) is adopted to perform benign and malignant classification on the extracted features of the pulmonary nodule. The improved deep
Boltzmann machine-based pulmonary nodule
feature extraction method is superior to a traditional
feature extraction method. With the method adopted, the complexity of
manual extraction and the difference of
feature selection can be avoided, and references can be provided for
clinical diagnosis.