Automated pulmonary nodule classification from low-dose CT images using ERBNet: an ensemble learning approach.

ABSTRACT: The aim of this study was to develop a deep learning method for analyzing CT images with varying doses and qualities, aiming to categorize lung lesions into nodules and non-nodules. This study utilized the lung nodule analysis 2016 challenge dataset. Different low-dose CT (LDCT) images, including 10%, 20%, 40%, and 60% levels, were generated from the full-dose CT (FDCT) images. Five different 3D convolutional networks were developed to classify lung nodules from LDCT and reference FDCT images. The models were evaluated using 400 nodule and 400 non-nodule samples. An ensemble model was also developed to achieve a generalizable model across different dose levels. The model achieved an accuracy of 97.0% for nodule classification on FDCT images. However, the model exhibited relatively poor per

SUBMITTER: Ahmadyar Y 

PROVIDER: S-EPMC12402046 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature