Thyroid Nodules Classification in Medical Ultrasound Images using Deep Learning

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.

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Recognition of calcifications in thyroid nodules based on attention-gated collaborative supervision network of ultrasound images

Journal of X-Ray Science and Technology ◽

10.3233/xst-200740 ◽

2020 ◽

Vol 28 (6) ◽

pp. 1123-1139

Author(s):

Liqun Zhang ◽

Ke Chen ◽

Lin Han ◽

Yan Zhuang ◽

Zhan Hua ◽

...

Keyword(s):

Deep Learning ◽

Object Recognition ◽

Thyroid Nodules ◽

Ultrasound Images ◽

Training Methods ◽

Important Criterion ◽

Improve Accuracy ◽

Important Means ◽

Model Training ◽

Medical Dataset

BACKGROUND: Calcification is an important criterion for classification between benign and malignant thyroid nodules. Deep learning provides an important means for automatic calcification recognition, but it is tedious to annotate pixel-level labels for calcifications with various morphologies. OBJECTIVE: This study aims to improve accuracy of calcification recognition and prediction of its location, as well as to reduce the number of pixel-level labels in model training. METHODS: We proposed a collaborative supervision network based on attention gating (CS-AGnet), which was composed of two branches: a segmentation network and a classification network. The reorganized two-stage collaborative semi-supervised model was trained under the supervision of all image-level labels and few pixel-level labels. RESULTS: The results show that although our semi-supervised network used only 30% (289 cases) of pixel-level labels for training, the accuracy of calcification recognition reaches 92.1%, which is very close to 92.9% of deep supervision with 100% (966 cases) pixel-level labels. The CS-AGnet enables to focus the model’s attention on calcification objects. Thus, it achieves higher accuracy than other deep learning methods. CONCLUSIONS: Our collaborative semi-supervised model has a preferable performance in calcification recognition, and it reduces the number of manual annotations of pixel-level labels. Moreover, it may be of great reference for the object recognition of medical dataset with few labels.

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Ultrasound Medical Images Classification Based on Deep Learning Algorithms: A Review

10.54216/fpa.030102 ◽

2021 ◽

pp. 29-42

Author(s):

admin admin ◽

◽

Adnan Mohsin Abdulazeez

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Recurrent Neural Networks ◽

Ultrasound Image ◽

Smart Devices ◽

Learning Approach ◽

Ultrasound Images ◽

Medical Field ◽

Modern Methods

With the development of technology and smart devices in the medical field, the computer system has become an essential part of this development to learn devices in the medical field. One of the learning methods is deep learning (DL), which is a branch of machine learning (ML). The deep learning approach has been used in this field because it is one of the modern methods of obtaining accurate results through its algorithms, and among these algorithms that are used in this field are convolutional neural networks (CNN) and recurrent neural networks (RNN). In this paper we reviewed what have researchers have done in their researches to solve fetal problems, then summarize and carefully discuss the applications in different tasks identified for segmentation and classification of ultrasound images. Finally, this study discussed the potential challenges and directions for applying deep learning in ultrasound image analysis.

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Detection of Thyroid Nodules with Ultrasound Images Based on Deep Learning

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666191023104751 ◽

2020 ◽

Vol 16 (2) ◽

pp. 174-180 ◽

Cited By ~ 2

Author(s):

Xia Yu ◽

Hongjie Wang ◽

Liyong Ma

Keyword(s):

Deep Learning ◽

Thyroid Nodule ◽

Thyroid Nodules ◽

Detection Method ◽

Ultrasound Images ◽

True Negative ◽

Automated Method ◽

High Incidence ◽

Sensitivity Specificity ◽

Physician Workload

Background: Thyroid nodules are a common clinical entity with high incidence. Ultrasound is often employed to detect and evaluate thyroid nodules. The development of an efficient automated method to detect thyroid nodules using ultrasound has the potential to reduce both physician workload and operator-dependence. Objective: To study the method of automatic detection of thyroid nodules based on deep learning using ultrasound, and to obtain the detection method with higher accuracy and better performance. Methods: A total of 1200 ultrasound images of thyroid nodules and 800 ultrasound thyroid images without nodule are collected. An improved faster R-CNN based detection method of thyroid nodule is proposed. Instead of using VGG16 as the backbone, ResNet is employed as the backbone for faster R-CNN. SVM, CNN and Faster-RCNN methods are used for thyroid nodule detection test. Precision, sensitivity, specificity and F1-score indicators are used to evaluate the detection performance of different methods. Results: The method based on deep learning is superior to that based on SVM. Faster R-CNN method and the improved method are better than CNN method. Compared with VGG16 as the backbone, RestNet101 backbone based faster R-CNN method achieves better thyroid detection effect. From the accuracy index, the proposed method is 0.084, 0.032 and 0.019 higher than SVM, CNN and faster R-CNN, respectively. Similar results can be seen in precision, sensitivity, specificity and F1-Score indicators. Conclusion: The proposed method of deep learning achieves the best performance values with the highest true positive and true negative detection compared to other methods and performs best in the detection of thyroid nodules.

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Thyroid Nodule Classification for Physician Decision Support Using Machine Learning-Evaluated Geometric and Morphological Features

Sensors ◽

10.3390/s20216110 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6110

Author(s):

Elmer Jeto Gomes Ataide ◽

Nikhila Ponugoti ◽

Alfredo Illanes ◽

Simone Schenke ◽

Michael Kreissl ◽

...

Keyword(s):

Machine Learning ◽

Decision Support ◽

Thyroid Nodule ◽

Thyroid Nodules ◽

Diagnostic Process ◽

Cad System ◽

Physician Decision Support ◽

Visual Characteristics ◽

Nodule Classification

The classification of thyroid nodules using ultrasound (US) imaging is done using the Thyroid Imaging Reporting and Data System (TIRADS) guidelines that classify nodules based on visual and textural characteristics. These are composition, shape, size, echogenicity, calcifications, margins, and vascularity. This work aims to reduce subjectivity in the current diagnostic process by using geometric and morphological (G-M) features that represent the visual characteristics of thyroid nodules to provide physicians with decision support. A total of 27 G-M features were extracted from images obtained from an open-access US thyroid nodule image database. 11 significant features in accordance with TIRADS were selected from this global feature set. Each feature was labeled (0 = benign and 1 = malignant) and the performance of the selected features was evaluated using machine learning (ML). G-M features together with ML resulted in the classification of thyroid nodules with a high accuracy, sensitivity and specificity. The results obtained here were compared against state-of the-art methods and perform significantly well in comparison. Furthermore, this method can act as a computer aided diagnostic (CAD) system for physicians by providing them with a validation of the TIRADS visual characteristics used for the classification of thyroid nodules in US images.

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Automatic Malignant Thyroid Nodule Recognition in Ultrasound Images based on Deep Learning

E3S Web of Conferences ◽

10.1051/e3sconf/202018503021 ◽

2020 ◽

Vol 185 ◽

pp. 03021

Author(s):

Meng Zhou ◽

Rui Wang ◽

Peng Fu ◽

Yang Bai ◽

Ligang Cui

Keyword(s):

Deep Learning ◽

Thyroid Nodule ◽

Thyroid Nodules ◽

Data Augmentation ◽

Medical Training ◽

Ultrasound Image ◽

Learning Model ◽

Training Data ◽

Ultrasound Images ◽

Deep Learning Model

As the most common malignancy in the endocrine system, thyroid cancer is usually diagnosed by discriminating the malignant nodules from the benign ones using ultrasonography, whose interpretation results primarily depends on the subjectivity judgement of the radiologists. In this study, we propose a novel cascade deep learning model to achieve automatic objective diagnose during ultrasound examination for assisting radiologists in recognizing benign and malignant thyroid nodules. First, the simplified U-net is employed to segment the region of interesting (ROI) of the thyroid nodules in each frame of the ultrasound image automatically. Then, to alleviate the limitation that medical training data are relatively small in size, the improved Conditional Variational Auto-Encoder (CVAE) learning the probability distribution of ROI images is trained to generate new images for data augmentation. Finally, ResNet50 is trained with both original and generated ROI images. As consequence, the deep learning model formed by the trained U-net and trained Resnet-50 cascade can achieve malignant thyroid nodule recognition with the accuracy of 87.4%, the sensitivity of 92%, and the specificity of 86.8%.

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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.

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

Diagnostics ◽

10.3390/diagnostics11122209 ◽

2021 ◽

Vol 11 (12) ◽

pp. 2209

Author(s):

Hafiz Abbad Ur Rehman ◽

Chyi-Yeu Lin ◽

Shun-Feng Su

Keyword(s):

Deep Learning ◽

Thyroid Nodules ◽

Image Database ◽

The United States ◽

Learning Approach ◽

Ultrasound Images ◽

Fast Screening ◽

The World ◽

Competitive Model ◽

Reliable Methods

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.

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