Classification of Benign and Malignant Thyroid Nodules in Ul

发布时间：2021-11-27 05:08

　　随着计算机视觉技术的进步,医学图像处理已被广泛用于医疗诊断。近年来,为了提高癌症早期检测准确率并改善治疗的效果,特别是在脑癌、肺癌、乳腺癌等肿瘤结节的诊断。超声图像的分辨率较低,并且图像中显示肿瘤的区域通常是模糊的,如边缘模糊,形状不规则。甲状腺结节是人体内分泌系统常最见的疾病,自动实现结节的良恶性分类可以辅助医生进行相关疾病的诊断。本质上,大多数甲状腺结节是良性的,只有不到5%是恶性的。大多数现有技术都有一些局限性,因为这些技术是在有限的数据集下进行检查和评估并且没有实现自动分类。因此,为了更加准确的实现甲状腺结节良恶性自动分类,我们将卷积神经网络应用于甲状腺结节超声图像的分类。为了提高检测的准确性,我们使用了深度卷积神经网络VGG-16提取结节区域的特征用于分类。在本研究中,VGG-16被用于甲状腺结节的分类。我们在公共数据集和本地数据集上训练和测试了模型。该模型可以较为快速、可靠地对甲状腺癌结节的良恶性进行分类,在医学领域具有一定的应用价值。 

【文章来源】：华南理工大学广东省 211工程院校 985工程院校 教育部直属院校

【文章页数】：67 页

【学位级别】：硕士

【文章目录】：
摘要
ABSTRACT
CHAPTER 1 INTRODUCTION
    1.1 Medical Ultrasound Therapy
    1.2 Medical Ultrasound in Clinical Medicine
        1.2.1 Therapeutic application
    1.3 Thyroid Disease
        1.3.1 Thyroid Nodule & Cancer
        1.3.2 Types of Thyroid Cancer
    1.4 Thyroid Disorder Diagnosis
        1.4.1 Blood Test
        1.4.2 Imaging test
        1.4.3 Biopsy
CHAPTER 2 MACHINE LEARNING ALGORITHMS
    2.1 Approaches of Machine
        2.1.1 Supervised machine learning
        2.1.2 Unsupervised machine learning
        2.1.3 Semi-supervised learning
    2.2 Artificial Neural Networks (ANNs)
        2.2.1 Feed forward Neural Networks
        2.2.2 Recurrent Networks
    2.3 Neural Networks
        2.3.1 Convolutional Neural Networks
    2.4 CNN Architecture
        2.4.1 LeNet-5 (1998)
        2.4.2 AlexNet (2012)
        2.4.3 ZFNet (2013)
        2.4.4 Google Net/Inception (2014)
        2.4.5 VGGNet (2014)
        2.4.6 ResNet (2015)
    2.5 Recurrent Neural Networks
    2.6 Long Short-Term Memory
    2.7 Radial Basis Function Network
    2.8 Capsule Neural Network
    2.9 Bayesian Networks
    2.10 Support Vector Machines
CHAPTER 3 VGG 16 MODEL
    3.1 VGG-16
    3.2 Architecture
    3.3 Components of VGG 16
        3.3.1 Convolution layer
        3.3.2 Re LU layer
        3.3.3 Pooling layer
        3.3.4 Batch normalization layer
        3.3.5 Dropout
        3.3.6 Soft Max, Loss and Regularization
        3.3.7 Optimization
    3.4 Implementation Details
    3.5 Algorithm
        3.5.1 Forward Propagation
        3.5.2 Parameters Initialization
        3.5.3 Activation Functions
        3.5.4 Rectified Linear Unit (Re LU)
        3.5.5 Leaky Rectified Linear Unit
        3.5.6 Feed Forward
        3.5.7 Cost
        3.5.8 Back-Propagation
CHAPTER 4 EXPERIMENTAL DATASET & RESULTS
    4.1 Dataset
    4.2 Image Pre-processing
    4.3 Data Augmentation
    4.4 Software and Hardware
    4.5 Experimental Procedures
        4.5.1 Splitting Dataset:
        4.5.2 Load The Dataset
        4.5.3 Train the model
    4.6 Results
    4.7 Discussion
CHAPTER 5 CONCLUSION
    Future work
References
Acknowledgement
附件



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