Tumor Tissue Identification Technology for Predicting Recurrence of Lung Adenocarcinoma Using Convolutional Neural Network

Abstract

Pathologists visually observe hematoxylin and eosin (HE) stained images under a microscope to perform pathological diagnosis. However, it is not possible to sufficiently diagnose future recurrence after surgery by judging shape using HE stained specimens alone, it is difficult to properly formulate a treatment policy for patients. In order to accurately identify tumor recurrence, this study proposes a method of automatically identifying future recurrence in a pathological image by calculating features of RGB and LLL (Image obtained by copying the luminance image L to 3 channels) components and by using the parallel structure of feature extractors. The method consists of three steps: 1. Features of recurrence presence or absence of surgically resected lung adenocarcinoma IB are extracted from RGB and LLL components of HE stained image using a convolutional neural network (CNN), 2. a classifier is created so that those features identify recurrence presence or absence of lung adenocarcinoma IB by using the CNN, 3. the recurrence presence or absence of lung adenocarcinoma is judged by using the classifier. The experimental results using digital images of pathological tissue specimens of lung adenocarcinoma IB show improved identification accuracy.