Classification of Mouse Lung Metastatic Tumor with Deep Learning
10.4062/biomolther.2021.130
- Author:
Ha Neul LEE
1
;
Hong-Deok SEO
;
Eui-Myoung KIM
;
Beom Seok HAN
;
Jin Seok KANG
Author Information
1. Department of Biomedical, Laboratory Science, Namseoul University, Cheonan 31020, Republic of Korea
- Publication Type:Original Article
- From:Biomolecules & Therapeutics
2022;30(2):179-183
- CountryRepublic of Korea
- Language:English
-
Abstract:
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.
