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Detection of Thyroid Nodules Through Neural Networks and Processing of Echographic Images

机译:通过神经网络检测甲状腺结节和回声图像的处理

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The abnormal functioning of hormones produces the appearance of malformations in human bodies that must be detected early. In this manuscript, two proposals are presented for the identification of thyroid nodules in ultrasound images, using convolutional neural networks. For the network training, 400 images obtained from a medical center and stored in a database have been used. Free access software (Python and TensorFlow) has been used as part of the algorithm development, following the stages of image preprocessing, network training, filtering and layer construction. Results graphically present the incidence of people suffering from this health problem. In addition, based on the respective tests, it is identified that the system developed in Python has greater precision and accuracy, 90% and 81% respectively, than TensorFlow design. Through neural networks, the recognition up to 4 mm thyroid nodules is evidenced.
机译:荷尔蒙的异常功能产生了必须在早期检测的人体中的畸形的外观。在该稿件中,使用卷积神经网络,提出了两个提案用于鉴定超声图像中的甲状腺结节。对于网络培训,已经使用了从医疗中心获得并存储在数据库中的400个图像。沿着图像预处理,网络训练,过滤和层构建的阶段,自由访问软件(Python和Tensorflow)已被用作算法开发的一部分。结果以图形方式出现患有这种健康问题的人的发病率。此外,基于各自的测试,识别出Python中开发的系统分别具有比TensorFlow设计的更高的精度和精度,90%和81%。通过神经网络,可以证明高达4mm的甲状腺结节的识别。

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