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

Author(s):

Yuejun Liu*, Yifei Xu, Xiangzheng Kasi Meng, Xuguang Wang and Tianxu Bai   Pages 1 - 7 ( 7 )

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.

Method: 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

Affiliation:

School of Automation, Harbin University of Science and Technology, Harbin, School of Automation, Harbin University of Science and Technology, Harbin, Department of Nuclear Medicine, Heilongjiang Provincial Hospital, Harbin, Department of Nuclear Medicine, Heilongjiang Provincial Hospital, Harbin, Department of Nuclear Medicine, Heilongjiang Provincial Hospital, Harbin



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