Wavelet decomposition facilitates training on small datasets for medical image classification by deep learning.

ABSTRACT: The adoption of low-dose computed tomography (LDCT) as the standard of care for lung cancer screening results in decreased mortality rates in high-risk population while increasing false-positive rate. Convolutional neural networks provide an ideal opportunity to improve malignant nodule detection; however, due to the lack of large adjudicated medical datasets these networks suffer from poor generalizability and overfitting. Using computed tomography images of the thorax from the National Lung Screening Trial (NLST), we compared discrete wavelet transforms (DWTs) against convolutional layers found in a CNN in order to evaluate their ability to classify suspicious lung nodules as either malignant or benign. We explored the use of the DWT as an alternative to the convolutional operations with

SUBMITTER: Masquelin AH 

PROVIDER: S-EPMC7957953 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature