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

Diagnostics ◽  
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.

scholarly journals Thyroid Nodules Classification in Medical Ultrasound Images using Deep Learning

2020 ◽  
Vol 9 (7) ◽  
pp. 1211-1215
Keyword(s):  
Deep Learning ◽  
Thyroid Nodule ◽  
Thyroid Nodules ◽  
Second Opinion ◽  
Learning Approach ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Cad System ◽  
Improve Accuracy

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.

Machine Learning as a Silent Observer of Advanced Geoscience Interpretation

2021 ◽  
Author(s):  
Muhammad Sajid
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Transfer Learning ◽  
Learning Performance ◽  
Learning Approach ◽  
Seismic Fault ◽  
Facies Classification ◽  
The World ◽  
Learning Machine ◽  
Fault Interpretation

Abstract Machine learning is proving its successes in all fields of life including medical, automotive, planning, engineering, etc. In the world of geoscience, ML showed impressive results in seismic fault interpretation, advance seismic attributes analysis, facies classification, and geobodies extraction such as channels, carbonates, and salt, etc. One of the challenges faced in geoscience is the availability of label data which is one of the most time-consuming requirements in supervised deep learning. In this paper, an advanced learning approach is proposed for geoscience where the machine observes the seismic interpretation activities and learns simultaneously as the interpretation progresses. Initial testing showed that through the proposed method along with transfer learning, machine learning performance is highly effective, and the machine accurately predicts features requiring minor post prediction filtering to be accepted as the optimal interpretation.

A deep learning‐based method for detecting and classifying the ultrasound images of suspicious thyroid nodules

2021 ◽  
Author(s):  
Zijian Zhao ◽  
Congmin Yang ◽  
Qian Wang ◽  
Huawei Zhang ◽  
Linlin Shi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Thyroid Nodules ◽  
Ultrasound Images

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

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.

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

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.

Evaluation of a deep learning‐based computer‐aided diagnosis system for distinguishing benign from malignant thyroid nodules in ultrasound images

2020 ◽  
Vol 47 (9) ◽  
pp. 3952-3960 ◽  
Author(s):  
Chao Sun ◽  
Yukang Zhang ◽  
Qing Chang ◽  
Tianjiao Liu ◽  
Shaohang Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Thyroid Nodules ◽  
Computer Aided Diagnosis ◽  
Ultrasound Images ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Comparison Study of Radiomics and Deep Learning-Based Methods for Thyroid Nodules Classification Using Ultrasound Images

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 52010-52017 ◽  
Author(s):  
Yongfeng Wang ◽  
Wenwen Yue ◽  
Xiaolong Li ◽  
Shuyu Liu ◽  
Lehang Guo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Thyroid Nodules ◽  
Ultrasound Images ◽  
Comparison Study

scholarly journals Quantifying Seagrass Distribution in Coastal Water with Deep Learning Models

2020 ◽  
Vol 12 (10) ◽  
pp. 1581 ◽  
Author(s):  
Daniel Perez ◽  
Kazi Islam ◽  
Victoria Hill ◽  
Richard Zimmerman ◽  
Blake Schaeffer ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Transfer Learning ◽  
Satellite Images ◽  
Support Vector ◽  
Learning Approach ◽  
Learning Models ◽  
Learning Techniques ◽  
The World ◽  
New Locations

Coastal ecosystems are critically affected by seagrass, both economically and ecologically. However, reliable seagrass distribution information is lacking in nearly all parts of the world because of the excessive costs associated with its assessment. In this paper, we develop two deep learning models for automatic seagrass distribution quantification based on 8-band satellite imagery. Specifically, we implemented a deep capsule network (DCN) and a deep convolutional neural network (CNN) to assess seagrass distribution through regression. The DCN model first determines whether seagrass is presented in the image through classification. Second, if seagrass is presented in the image, it quantifies the seagrass through regression. During training, the regression and classification modules are jointly optimized to achieve end-to-end learning. The CNN model is strictly trained for regression in seagrass and non-seagrass patches. In addition, we propose a transfer learning approach to transfer knowledge in the trained deep models at one location to perform seagrass quantification at a different location. We evaluate the proposed methods in three WorldView-2 satellite images taken from the coastal area in Florida. Experimental results show that the proposed deep DCN and CNN models performed similarly and achieved much better results than a linear regression model and a support vector machine. We also demonstrate that using transfer learning techniques for the quantification of seagrass significantly improved the results as compared to directly applying the deep models to new locations.

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