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. 2021 Nov 26;11(12):2209.
doi: 10.3390/diagnostics11122209.

Deep Learning Based Fast Screening Approach on Ultrasound Images for Thyroid Nodules Diagnosis

Hafiz Abbad Ur Rehman  1 , Chyi-Yeu Lin  1 , Shun-Feng Su  2
Affiliations

Deep Learning Based Fast Screening Approach on Ultrasound Images for Thyroid Nodules Diagnosis

Hafiz Abbad Ur Rehman et al. Diagnostics (Basel). .

Abstract

Thyroid nodules are widespread in the United States and the rest of the world, with a prevalence ranging from 19 to 68%. The problem with nodules is whether they are malignant or benign. Ultrasonography is currently recommended as the initial modality for evaluating thyroid nodules. However, obtaining a good diagnosis from ultrasound imaging depends entirely on the radiologists levels of experience and other circumstances. There is a tremendous demand for automated and more reliable methods to screen ultrasound images more efficiently. This research proposes an efficient and quick detection deep learning approach for thyroid nodules. An open and publicly available dataset, Thyroid Digital Image Database (TDID), is used to determine the robustness of the suggested method. Each image is formatted into a pyramid tile-based data structure, which the proposed VGG-16 model evaluates to provide segmentation results for nodular detection. The proposed method adopts a top-down approach to hierarchically integrate high- and low-level features to distinguish nodules of varied sizes by employing fuse features effectively. The results demonstrated that the proposed method outperformed the U-Net model, achieving an accuracy of 99%, and was two times faster than the competitive model.

Keywords: deep learning; healthcare; medical diagnosis; thyroid nodule.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Schematic block diagram of the proposed methodology.
Figure 2
Figure 2
Proposed backbone VGG-16 architecture.
Figure 3
Figure 3
Original image and its corresponding GT masks.
Figure 4
Figure 4
Segmentation output results of the proposed and benchmark methods.
See this image and copyright information in PMC

References

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