Open AccessThyroid Nodules Classification in Medical Ultrasound Images using Deep Learning
Author(s) -
Mayuresh B. Gulame,
Dr.Vaibhav. V. Dixit
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.g5163.059720
Subject(s) - thyroid nodules , convolutional neural network , nodule (geology) , artificial intelligence , deep learning , computer science , feature extraction , classifier (uml) , ultrasound , thyroid , radiology , cad , computer aided diagnosis , second opinion , medicine , pattern recognition (psychology) , pathology , engineering , paleontology , engineering drawing , biology
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.