A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

Author(s): Yuejun Liu*, Yifei Xu, Xiangzheng Meng, Xuguang Wang, Tianxu Bai.

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews

Volume 16 , Issue 3 , 2020

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Graphical Abstract:


Abstract:

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success.

Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared.

Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods.

Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best.

Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

Keywords: Deep learning, thyroid disease, image processing, convolutional neural network, SPECT, auxiliary diagnosis.

[1]
Cooper DS, Doherty GM, Haugen BR, et al. Management guidelines for patients with thyroid nodules and differentiated thyroid cancer. Thyroid 2006; 16(2): 109-42.
[http://dx.doi.org/10.1089/thy.2006.16.109] [PMID: 16420177]
[2]
Parry Z, Macnab R. Thyroid disease and thyroid surgery. Anaesth Intensive Care 2017; 18(10): 488-195.
[http://dx.doi.org/10.1016/j.mpaic.2017.06.015]
[3]
Maniakas A, Davies L, Zafereo ME. Thyroid disease around the world. Otolaryngol Clin North Am 2018; 51(3): 631-42.
[http://dx.doi.org/10.1016/j.otc.2018.01.014] [PMID: 29548512]
[4]
Chang C, Hong Y, Chung P, Tseng C. A neural network for thyroid segmentation and volume estimation in CT images. IEEE Comput Intell Mag 2011; 6(4): 43-55.
[http://dx.doi.org/10.1109/MCI.2011.942756]
[5]
Chang CY, Chen SJ, Tsai SF. Application of support-vector-machine-based method for feature selection and classification of thyroid nodules in ultrasound images. Pattern Recognit 2010; 43(10): 3494-506.
[http://dx.doi.org/10.1016/j.patcog.2010.04.023]
[6]
Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput 2006; 18(7): 1527-54.
[http://dx.doi.org/10.1162/neco.2006.18.7.1527] [PMID: 16764513]
[7]
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521(7553): 436-44.
[http://dx.doi.org/10.1038/nature14539] [PMID: 26017442]
[8]
Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw 2015; 61(1): 85-117.
[http://dx.doi.org/10.1016/j.neunet.2014.09.003] [PMID: 25462637]
[9]
Ker J, Wang L, Rao J, Lim T. Deep learning applications in medical image analysis. IEEE Access 2018; 6(1): 9375-89.
[http://dx.doi.org/10.1109/ACCESS.2017.2788044]
[10]
Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng 2017; 19(1): 221-48.
[http://dx.doi.org/10.1146/annurev-bioeng-071516-044442] [PMID: 28301734]
[11]
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42(1): 60-88.
[http://dx.doi.org/10.1016/j.media.2017.07.005] [PMID: 28778026]
[12]
Gong YC, Wang LW, Guo RQ, Lazebnik S. Multi-scale orderless pooling of deep convolutional activation features. Proceedings of the 13th European Conference on Computer Vision (ECCV2014). 2014 September 6-12; Zurich, Switzerland. 392-407.
[http://dx.doi.org/10.1007/978-3-319-10584-0_26]
[13]
Sarraf S, Tofighi G. Classification of Alzheimer’s disease using fMRI data and deep learning convolutional neural networks. IEEE Trans Med Imaging 2016; 29(3): 1026-31.
[14]
Ma J, Wu F, Jiang T, Zhu J, Kong D. Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images. Med Phys 2017; 44(5): 1678-91.
[http://dx.doi.org/10.1002/mp.12134] [PMID: 28186630 ]
[15]
Liu T, Xie S, Xu J, Niu L, Sun W. Classification of thyroid nodules in ultrasound images using deep model based transfer learning and hybrid features. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Mar 05-09, 2017; New Orleans, USA. 2017; 919-23.
[http://dx.doi.org/10.1109/ICASSP.2017.7952290]
[16]
Ma J, Wu F, Zhu J, Xu D, Kong D. A pre-trained convolutional neural network based method for thyroid nodule diagnosis. Ultrasonics 2017; 73(1): 221-30.
[http://dx.doi.org/10.1016/j.ultras.2016.09.011]
[17]
Ma LY, Ma CK, Liu YJ, Wang XG, Xie W. Diagnosis of thyroid diseases using SPECT image based on convolutional neural network. J Med Imag Health 2018; 8(8): 1684-9.
[http://dx.doi.org/10.1166/jmihi.2018.2493]
[18]
Li H, Wang J, Shi Y, et al. An improved deep learning approach for detection of thyroid papillary cancer in ultrasound images. Sci Rep 2018; 8(4): 6600.
[http://dx.doi.org/10.1038/s41598-018-25005-7]
[19]
Wang Y, Ke W, Wan P. A method of ultrasonic image recognition for thyroid papillary carcinoma based on deep convolution neural network. Neuroquantology 2018; 16(5): 757-68.
[http://dx.doi.org/10.14704/nq.2018.16.5.1306]
[20]
Luo W, Zhang YF, Zhou XD. Development of medical images in differentiating benign from malignant thyroid nodules. Curr Med Imaging 2016; 12(4): 248-56.
[http://dx.doi.org/10.2174/157340561204161025212937]
[21]
Dolores MR, Sonia AM, Antonio SH, et al. Thyroid nodules: Too many fine needle biopsies? Curr Med Imaging 2018; 14(5): 725-31.
[http://dx.doi.org/10.2174/1573405613666170726100431]
[22]
Chi J, Walia E, Babyn P, Wang J, Groot G, Eramian M. Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network. J Digit Imaging 2017; 30(4): 477-86.
[http://dx.doi.org/10.1007/s10278-017-9997-y] [PMID: 28695342]
[23]
Shin HC, Roth HR, Gao M, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 2016; 35(5): 1285-98.
[http://dx.doi.org/10.1109/TMI.2016.2528162] [PMID: 26886976]
[24]
Nishio M, Sugiyama O, Yakami M, et al. Computer-aided diagnosis of lung nodule classification be-tween benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning. PLOS One 2018; 13(7): e0200721
[http://dx.doi.org/10.1371/journal.pone.0200721]
[25]
Kaehler A, Bradski G. Learning OpenCV 3: computer vision in C++ with the OpenCV library. O'Reilly Media 2016.
[26]
Hou X, Fan X, Yang L, et al. Thyroid imaging report and data system classification and diagnosis of benign and malignant thyroid nodules. J Chin Med Imag 2015; 23(7): 489-93.


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Article Details

VOLUME: 16
ISSUE: 3
Year: 2020
Page: [199 - 205]
Pages: 7
DOI: 10.2174/1573405615666190115155223
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