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KMID : 0606920220300020179
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Biomolecules & Therapeutics
2022 Volume.30 No. 2 p.179 ~ p.183
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Classification of Mouse Lung Metastatic Tumor with Deep Learning
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Lee Ha-Neul
Seo Hong-Deok
Kim Eui-Myoung
Han Beom-Seok
Kang Jin-Seok
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Abstract
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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.
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KEYWORD
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Mouse, Lung tumor, Digital pathology, Classification, Deep learning
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