Classification of Adenomatous Hyperplasia Thyroid Nodules Using the Features Extracted from Ultrasound Images

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.

Download Full-text

Classification of thyroid nodules using ultrasound images

Bioinformation ◽

10.6026/97320630016145 ◽

2020 ◽

Vol 16 (2) ◽

pp. 145-148

Author(s):

T Manivannan ◽

Keyword(s):

Thyroid Nodules ◽

Ultrasound Images

Download Full-text

Classification of thyroid nodules in ultrasound images using deep model based transfer learning and hybrid features

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp.2017.7952290 ◽

2017 ◽

Cited By ~ 25

Author(s):

Tianjiao Liu ◽

Shuaining Xie ◽

Jing Yu ◽

Lijuan Niu ◽

Weidong Sun

Keyword(s):

Transfer Learning ◽

Thyroid Nodules ◽

Ultrasound Images ◽

Hybrid Features ◽

Model Based ◽

Deep Model

Download Full-text

Automatic Segmentation and Classification of Thyroid Nodules in Ultrasound Images with Convolutional Neural Networks

Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data - Lecture Notes in Computer Science ◽

10.1007/978-3-030-71827-5_14 ◽

2021 ◽

pp. 109-115

Author(s):

Mingyu Wang ◽

Chenglang Yuan ◽

Dasheng Wu ◽

Yinghou Zeng ◽

Shaonan Zhong ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Thyroid Nodules ◽

Automatic Segmentation ◽

Ultrasound Images

Download Full-text

Detection and classification of thyroid nodule using Shearlet coefficients and support vector machine

International Journal of Engineering & Technology ◽

10.14419/ijet.v6i3.7705 ◽

2017 ◽

Vol 6 (3) ◽

pp. 50

Author(s):

Nanda S. ◽

Sukumar M.

Keyword(s):

Support Vector Machine ◽

Thyroid Nodule ◽

Thyroid Nodules ◽

Nearest Neighbor ◽

Support Vector ◽

Textural Features ◽

Ultrasound Images ◽

K Nearest Neighbor ◽

Benign Nodules

Thyroid nodules have diversified internal components and dissimilar echo patterns in ultrasound images. Textural features are used to characterize these echo patterns. This paper presents a classification scheme that uses shearlet transform based textural features for the classification of thyroid nodules in ultrasound images. The study comprised of 60 thyroid ultrasound images (30 with benign nodules and 30 with malignant nodules). Total of 22 features are extracted. Support vector machine (SVM) and K nearest neighbor (KNN) are used to differentiate benign and malignant nodules. The diagnostic sensitivity, specificity, F1_score and accuracy of both the classifiers are calculated. A comparative study has been carried out with respect to their performances. The sensitivity of SVM with radial basis function (RBF) kernel is 100% as compared to that of KNN with 96.33%. The proposed features can increase the accuracy of the classifier and decrease the rate of misdiagnosis in thyroid nodule classification.

Download Full-text