Evaluation of transfer learning of pre-trained CNNs applied to breast cancer detection on infrared images

Chaves Esdras; Goncalves Caroline B.; Albertini Marcelo K.; Lee Soojeong; Jeon Gwanggil; Fernandes Henrique C.

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Evaluation of transfer learning of pre-trained CNNs applied to breast cancer detection on infrared images

机译：预培训的CNN的转移学习评估应用于红外图像乳腺癌检测

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Breast cancer accounts for the highest number of female deaths worldwide. Early detection of the disease is essential to increase the chances of treatment and cure of patients. Infrared thermography has emerged as a promising technique for diagnosis of the disease due to its low cost and that it does not emit harmful radiation, and it gives good results when applied in young women. This work uses convolutional neural networks in a database of 440 infrared images of 88 patients, classifying them into two classes: normal and pathology. During the training of the networks, we use transfer learning of the following convolutional neural network architectures: AlexNet, GoogLeNet, ResNet-18, VGG-16, and VGG-19. Our results show the great potential of using deep learning techniques combined with infrared images in the aid of breast cancer diagnosis. (C) 2020 Optical Society of America

机译：乳腺癌占全世界女性死亡数量的最多。早期发现这种疾病对于增加治疗和治疗患者的机会是必不可少的。红外热成像由于其低成本而被出现为诊断疾病的有希望的技术，并且它不会发出有害的辐射，并且在年轻女性中施用良好的结果。这项工作在88名患者的440个红外图像的数据库中使用卷积神经网络，将它们分为两类：正常和病理学。在网络培训期间，我们使用以下卷积神经网络架构的转移学习：AlexNet，Googlenet，Reset-18，VGG-16和VGG-19。我们的结果表明，借助乳腺癌诊断，使用深层学习技术的潜力很大。（c）2020美国光学学会

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来源
《Applied optics》 |2020年第17期|共6页
作者
Chaves Esdras; Goncalves Caroline B.; Albertini Marcelo K.; Lee Soojeong; Jeon Gwanggil; Fernandes Henrique C.;
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Univ Fed Uberlandia Sch Comp Sci Uberlandia MG Brazil;

Univ Fed Uberlandia Sch Comp Sci Uberlandia MG Brazil;

Univ Fed Uberlandia Sch Comp Sci Uberlandia MG Brazil;

Sejong Univ Dept Comp Engn Seoul South Korea;

Incheon Natl Univ Dept Embedded Syst Engn Incheon South Korea;

Univ Fed Uberlandia Sch Comp Sci Uberlandia MG Brazil;

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Evaluation of transfer learning of pre-trained CNNs applied to breast cancer detection on infrared images

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