Biomolecules & Therapeutics  https://doi.org/10.4062/biomolther.2021.130
Classification of Mouse Lung Metastatic Tumor with Deep Learning
Ha Neul Lee1, Hong-Deok Seo2, Eui-Myoung Kim3, Beom Seok Han4 and Jin Seok Kang1,*
1Department of Biomedical, Laboratory Science, Namseoul University, Cheonan 31020,
2Department of Industrial Promotion, Spatial Information Industry Promotion Agency, Seongnam 13487,
3Department of Spatial Information Engineering, Namseoul University, Cheonan 31020,
4Department of Pharmaceutical Engineering, Hoseo University, Asan 31499, Republic of Korea
*E-mail: kang@nsu.ac.kr
Tel: +82-41-580-2721, Fax: +82-41-580-2932
Received: August 2, 2021; Revised: August 31, 2021; Accepted: September 13, 2021; Published online: November 2, 2021.
© The Korean Society of Applied Pharmacology. All rights reserved.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
Keywords: Mouse, Lung tumor, Digital pathology, Classification, Deep learning


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