Classification of Mouse Lung Metastatic Tumor with Deep Learning

Traditionally, pathologists microscopically examine tissue sections to detect pathological lesions; the many slides that must be evaluated impose severe work burdens. Also, diagnostic accuracy varies by pathologist training and experience; better diagnostic tools are required. Given the rapid develo...

Full description

Saved in:
Bibliographic Details
Published in:Biomolecules & therapeutics 2022-03, Vol.30 (2), p.179-183
Main Authors: Lee, Ha Neul, Seo, Hong-Deok, Kim, Eui-Myoung, Han, Beom Seok, Kang, Jin Seok
Format: Article
Language:Korean
Subjects:
Classification
Deep learning
Digital pathology
Lung tumor
Mouse
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Traditionally, pathologists microscopically examine tissue sections to detect pathological lesions; the many slides that must be evaluated impose severe work burdens. Also, diagnostic accuracy varies by pathologist training and experience; better diagnostic tools are required. Given the rapid development of computer vision, automated deep learning is now used to classify microscopic images, including medical images. Here, we used a Inception-v3 deep learning model to detect mouse lung metastatic tumors via whole slide imaging (WSI); we cropped the images to 151 by 151 pixels. The images were divided into training (53.8%) and test (46.2%) sets (21,017 and 18,016 images, respectively). When images from lung tissue containing tumor tissues were evaluated, the model accuracy was 98.76%. When images from normal lung tissue were evaluated, the model accuracy (“no tumor”) was 99.87%. Thus, the deep learning model distinguished metastatic lesions from normal lung tissue. Our approach will allow the rapid and accurate analysis of various tissues.
ISSN:1976-9148
2005-4483