Biomol Ther.  2022 Mar;30(2):179-183. 10.4062/biomolther.2021.130.

Classification of Mouse Lung Metastatic Tumor with Deep Learning

• Lee HN
• Seo HD
• Kim EM
• Han BS
• Kang JS

• KMID: 2526820
• DOI: http://doi.org/10.4062/biomolther.2021.130

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.

Keyword

Mouse; Lung tumor; Digital pathology; Classification; Deep learning