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.
A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT