基于深度卷积网络的肺癌医学诊断预测模型——一种深度药物基因组学方法:肺癌的深度诊断

Akshay Iyer, Hima Vyshnavi A M, Krishnan Namboori P K
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引用次数: 11

摘要

最近引入的深度学习环境已被用于后药物基因组学预测肺癌易感性。成熟的预训练模型VGG19已被用于从肺癌的PTEN、EGFR、ERBB2、BRAF和CDKN2A特异性病理图像中提取信息。该模型发现适合于预测这些突变,特别是针对印度人群。一种由特定遗传特征测试和基于深度卷积网络的图像分类组成的模型被认为是一种快速、廉价和有效地预测印度人群肺癌突变的巧妙技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Convolution Network Based Prediction Model For Medical Diagnosis Of Lung Cancer - A Deep Pharmacogenomic Approach : deep diagnosis for lung cancer
The recently introduced deep learning environment has been used in post pharmacogenomic predictions on proneness to lung cancer. The well-established pre-trained model, VGG19 has been used to extract information from the pathological images specific to PTEN, EGFR, ERBB2, BRAF and CDKN2A for Lung cancer. The model finds to be suitable for the prediction of these mutations, specific to Indian populations. A model consisting of testing of specific genetic signatures and the deep convolution network based image classification has been suggested as an ingenious technique for the fast, cheap and effective predictions on mutations of lung cancer among Indian populations.
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