Thyroid Nodules Classification in Medical Ultrasound Images using Deep Learning
Mayuresh B. Gulame1, Vaibhav V. Dixit2

1Mayuresh Gulame*, Research scholar in G H R College of Engineering and Management, Pune and Assistant Professor in Trinity academy of Engineering, Pune, India.
2Dr. Vaibhav. V. Dixit, Director, RMD Sinhgad Technical Institutes Campus, Warje, Pune, India.
Manuscript received on April 20, 2020. | Revised Manuscript received on April 30, 2020. | Manuscript published on May 10, 2020. | PP: 1211-1215 | Volume-9 Issue-7, May 2020. | Retrieval Number: G5163059720/2020©BEIESP | DOI: 10.35940/ijitee.G5163.059720
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Abstract: Ultrasound scanning is most excellent significant diagnosis techniques utilized for thyroid nodules identification. A thyroid nodule is unnecessary cells that can develop in your base of neck which can be normal or cancerous. Many Computer added diagnosis systems (CAD) have been developed as a second opinion for radiologist. The thyroid nodules classification using machine learning and deep learning approach is latest trend which is using to improve accuracy for differentiation of thyroid nodules from benign and malignant type. In this paper we review the most recent work on CAD system which uses different feature extraction technique and classifier used for thyroid nodules classification with deep learning approach. This paper we illustrate the result obtained by these studies and highlight the limitation of each proposed methods. Moreover we summarize convolution neural network (CNN) architecture for classification of thyroid nodule. This literature review is meant at researcher but it also useful for radiologist who is interesting in CAD tool in ultrasound imaging for second opinion. 
Keywords: CAD system, CNN, Malignancy, Thyroid nodules, Ultrasound imaging.
Scope of the Article: Classification