A neural pathomics framework for classifying colorectal cancer histopathology images based on wavelet multi-scale texture analysis.

ABSTRACT: Colorectal cancer (CRC) constitutes the third most commonly diagnosed cancer in males and the second in females. Precise histopathological classification of CRC tissue pathology is the cornerstone not only for diagnosis but also for patients' management decision making. An automated system able to accurately classify different CRC tissue regions may increase diagnostic precision and alleviate clinical workload. However, tissue classification is a challenging task due to the variability in morphological and textural characteristics present in histopathology images. In this study, an artificial neural network was trained to classify between eight classes of CRC tissue image patches derived from a public dataset with 5000 CRC histopathology image tiles. A total of 532 multi-level pathomics fe

SUBMITTER: Trivizakis E 

PROVIDER: S-EPMC8324876 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature