Deep convolutional neural networks in thyroid disease detection: A multi-classification comparison by ultrasonography and computed tomography.

• Multi-classifying thyroid disease leads to enhanced diagnostic decision-making. • Ultrasound image modalities outperform CT scans regarding CAD detection. • Xception generalizes well to different medical image modalities. • Adoptions of CAD in the clinic need to earn confidence among doctors and patients. [Display omitted] Background and Objective: As one of the largest endocrine organs in the human body, the thyroid gland regulates daily metabolism. Early detection of thyroid disease leads to reduced mortality rates. The diagnosis of thyroid disease is usually made by radiologists and pathologists, which heavily relies on their experience and expertise. To mitigate human false-positive diagnostic rates, this paper proves that deep learning-driven techniques yield promising performance for automatic detection of thyroid diseases which offers clinicians assistance regarding diagnostic decision-making. Method: This research study is the first of its kind, which adopts two pre-operative medical image modalities for multi-classifying thyroid disease types (i.e., normal, thyroiditis, cystic, multi-nodular goiter, adenoma, and cancer). Using the current state-of-the-art performing deep convolutional neural network (CNN) architecture, this study builds a thyroid disease diagnostic model for distinguishing among the disease types. Results: The model obtains unprecedented performance for both medical image sets, and it reaches an accuracy of 0.972 and 0.942 for ultrasound images and computed tomography (CT) scans correspondingly. Conclusion: The experimental results illustrate that the selected CNN can be adapted to both image modalities, indicating the feasibility of the deep learning model and emphasizing its further applications in clinics. [ABSTRACT FROM AUTHOR]