Efficient pulmonary nodules classification using radiomics and different artificial intelligence strategies.

ABSTRACT:

Objectives

This study aimed to explore and develop artificial intelligence approaches for efficient classification of pulmonary nodules based on CT scans.

Materials and methods

A number of 1007 nodules were obtained from 551 patients of LIDC-IDRI dataset. All nodules were cropped into 64 × 64 PNG images , and preprocessing was carried out to clean the image from surrounding non-nodular structure. In machine learning method, texture Haralick and local binary pattern features were extracted. Four features were selected using principal component analysis (PCA) algorithm before running classifiers. In deep learning, a simple CNN model was constructed and transfer learning was applied using VGG-16 and VGG-19, DenseNet-121 and DenseNet-169 and ResNet as pre-trained models with fin

SUBMITTER: Saied M 

PROVIDER: S-EPMC10195968 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature