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Classification of Deep Convolutional Neural Network in Thyroid Ultrasound Images

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机构： [1]Hebei Med Univ, Hosp 3, Shijiazhuang 050051, Hebei, Peoples R China [2]Hebei Normal Univ, Shijiazhuang 050024, Hebei, Peoples R China [3]Hebei Med Univ, Hosp 4, Shijiazhuang 050051, Hebei, Peoples R China [4]First Hosp Shijiazhuang, Shijiazhuang 050011, Hebei, Peoples R China [5]Coll Engn & Technol, Dept IT, Dindigul 629702, Tamil Nadu, India

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关键词： Classification Convolutional Neural Network Thyroid Ultrasound Images Deep Learning

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Objective: To explore the application of deep convolutional neural network theory in thyroid ultrasound image system analysis and eigenvalue extraction to help medically predict the patient's condition. Methods: The thyroid color ultrasound image dataset of our hospital was selected as the training and test samples. The comparison experiment was designed in the deep convolutional neural network learning framework to test the feasibility of the method. Results: Image information classification based on deep neural network algorithm can predict thyroid nodule lesions well, and has good accuracy in the classification test of benign and malignant nodules. Conclusion: The clinical application of deep learning method and thyroid ultrasound image feature value extraction and system analysis can improve the accuracy of clinical thyroid benign and malignant classification.

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出版当年[2020]版：

大类 | 4 区医学

小类 | 4 区数学与计算生物学 4 区核医学

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无

影响因子： 0 最新[2024版] 0 最新五年平均 0 出版当年[2020版] 0 出版当年五年平均 0.659 出版前一年[2019版]

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第一作者机构： [1]Hebei Med Univ, Hosp 3, Shijiazhuang 050051, Hebei, Peoples R China

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