Improving the Subtype Classification of Non-small Cell Lung Cancer by Elastic Deformation Based Machine Learning.

ABSTRACT: Non-invasive image-based machine learning models have been used to classify subtypes of non-small cell lung cancer (NSCLC). However, the classification performance is limited by the dataset size, because insufficient data cannot fully represent the characteristics of the tumor lesions. In this work, a data augmentation method named elastic deformation is proposed to artificially enlarge the image dataset of NSCLC patients with two subtypes (squamous cell carcinoma and large cell carcinoma) of 3158 images. Elastic deformation effectively expanded the dataset by generating new images, in which tumor lesions go through elastic shape transformation. To evaluate the proposed method, two classification models were trained on the original and augmented dataset, respectively. Using augmented datas

SUBMITTER: Gao Y 

PROVIDER: S-EPMC8329138 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature