• Logo
  • HamaraJournals

Deep Learning Applications in Analyzing Ultrasound Images of Thyroid Nodules: Protocol for a Systematic Review

Yasaman Sharifi, Saeed Eslami HassanAbady, Morteza Danai Ashgzari, Mahdi Sargolzaei
3

Views

XML

Abstract

Introduction: Ultrasound images are one of the main contributors for evaluating of thyroid nodules. However, reading ultrasound imaging is not easy and strongly depends to doctors’ experiences. Therefore, a CAD system could assist doctors in evaluating thyroid ultrasound images to reduce the impact of subjective experience on the diagnostic results.

Objective: with the best of our knowledge there is not any articles that actually provide a systematic review of deep learning application in analyzing ultrasound images of thyroid nodules and Hence, a comprehensive review of studies in this field can be useful, therefore the protocol of this systematic Review will be presented to reach this goal.

Method: This protocol includes five stages: research questions definition, search strategy design, study selection, quality assessment and data extraction. We developed search for relevant English language articles using the PubMed, Scopus and Science Direct. Inclusion and exclusion criteria were defined and flow diagram is conducted, from 623 studies retrieved, 27 studies were included, after quality assessment data was extracted based on defined categories.

Result: The result of this systematic review can help researchers by comprehensive view and the summary of evidence to present new ideas and further research and represent a state of the art in this field.

Conclusion: in this study a protocol was used for doing a systematic review on various deep learning applications in thyroid ultrasound such as feature selection, classification, localization, detection and segmentation. Articles were screened based on the following items: study and patient information, dataset, method, results and comparison method.

References

Khachnaoui H, Guetari R, Khlifa N, editors. A review on Deep Learning in thyroid ultrasound Computer-Assisted Diagnosis systems2019.

Cooper D. American Thyroid Association (ATA) guidelines taskforce on thyroid nodules and differentiated thyroid cancer. Revised American Thyroid Association management guidelines for patients with thyroid nodules and differentiated thyroid cancer. Thyroid. 2009;19:1167-214.

Zhuang Y, Li C, Hua Z, Chen K, Lin JL. A novel TIRADS of US classification. BioMedical Engineering Online. 2018;17(1).

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. Journal of Digital Imaging. 2017;30(4):477-86.

Rajendra Acharya U, Swapna G, Vinitha Sree S, Molinari F, Gupta S, Bardales RH, et al. A review on ultrasound-based thyroid cancer tissue characterization and automated classification. Technology in Cancer Research and Treatment. 2014;13(4):289-301.

Li X, Wang S, Wei X, Zhu J, Yu R, Zhao M, et al., editors. Fully Convolutional Networks for Ultrasound Image Segmentation of Thyroid Nodules. 20th International Conference on High Performance Computing and Communications, 16th IEEE International Conference on Smart City and 4th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018; 2019.

LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015;521(7553):436.

Guan Q, Wang Y, Du J, Qin Y, Lu H, Xiang J, et al. Deep learning based classification of ultrasound images for thyroid nodules: a large scale of pilot study. Annals of translational medicine. 2019;7(7):137.

Kumar A, Kim J, Lyndon D, Fulham M, Feng D. An ensemble of fine-tuned convolutional neural networks for medical image classification. IEEE journal of biomedical and health informatics. 2016;21(1):31-40.

Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Briefings in bioinformatics. 2017;19(6):1236-46.

Lee J-G, Jun S, Cho Y-W, Lee H, Kim GB, Seo JB, et al. Deep learning in medical imaging: general overview. Korean journal of radiology. 2017;18(4):570-84.

Suzuki K. Overview of deep learning in medical imaging. Radiological physics and technology. 2017;10(3):257-73.

Bakator M, Radosav D. Deep learning and medical diagnosis: A review of literature. Multimodal Technologies and Interaction. 2018;2(3):47.

Cheng H-D, Shan J, Ju W, Guo Y, Zhang L. Automated breast cancer detection and classification using ultrasound images: A survey. Pattern recognition. 2010;43(1):299-317.

Huang Q, Luo Y, Zhang Q. Breast ultrasound image segmentation: a survey. International journal of computer assisted radiology and surgery. 2017;12(3):493-507.

Jabarulla MY, Lee H-N. Computer aided diagnostic system for ultrasound liver images: A systematic review. Optik. 2017;140:1114-26.

Sollini M, Cozzi L, Chiti A, Kirienko M. Texture analysis and machine learning to characterize suspected thyroid nodules and differentiated thyroid cancer: Where do we stand? European journal of radiology. 2018;99:1-8.

Huang Q, Zhang F, Li X. Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey. BioMed Research International. 2018;2018.

Kitchenham B, Charters S. Guidelines for performing systematic literature reviews in software engineering. 2007.

Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS medicine. 2009;6(7):e1000097.

Malhotra R. A systematic review of machine learning techniques for software fault prediction. Applied Soft Computing. 2015;27:504-18.

Landis JR, Koch GG. The measurement of observer agreement for categorical data. biometrics. 1977:159-74.




DOI: http://dx.doi.org/10.30699/fhi.v9i1.220

Refbacks

  • There are currently no refbacks.