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http://dx.doi.org/10.4062/biomolther.2021.130

Classification of Mouse Lung Metastatic Tumor with Deep Learning  

Lee, Ha Neul (Department of Biomedical, Laboratory Science, Namseoul University)
Seo, Hong-Deok (Department of Industrial Promotion, Spatial Information Industry Promotion Agency)
Kim, Eui-Myoung (Department of Spatial Information Engineering, Namseoul University)
Han, Beom Seok (Department of Pharmaceutical Engineering, Hoseo University)
Kang, Jin Seok (Department of Biomedical, Laboratory Science, Namseoul University)
Publication Information
Biomolecules & Therapeutics / v.30, no.2, 2022 , pp. 179-183 More about this Journal
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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