Efficient Deep Learning Architecture for Detection and Recognition of Thyroid Nodules

Ultrasonography is widely used in the clinical diagnosis of thyroid nodules. Ultrasound images of thyroid nodules have different appearances, interior features, and blurred borders that are difficult for a physician to diagnose into malignant or benign types merely through visual recognition. The development of artificial intelligence, especially deep learning, has led to great advances in the field of medical image diagnosis. However, there are some challenges to achieve precision and efficiency in the recognition of thyroid nodules. In this work, we propose a deep learning architecture, you only look once v3 dense multireceptive fields convolutional neural network (YOLOv3-DMRF), based on YOLOv3. It comprises a DMRF-CNN and multiscale detection layers. In DMRF-CNN, we integrate dilated convolution with different dilation rates to continue passing the edge and the texture features to deeper layers. Two different scale detection layers are deployed to recognize the different sizes of the thyroid nodules. We used two datasets to train and evaluate the YOLOv3-DMRF during the experiments. One dataset includes 699 original ultrasound images of thyroid nodules collected from a local health physical center. We obtained 10,485 images after data augmentation. Another dataset is an open-access dataset that includes ultrasound images of 111 malignant and 41 benign thyroid nodules. Average precision (AP) and mean average precision (mAP) are used as the metrics for quantitative and qualitative evaluations. We compared the proposed YOLOv3-DMRF with some state-of-the-art deep learning networks. The experimental results show that YOLOv3-DMRF outperforms others on mAP and detection time on both the datasets. Specifically, the values of mAP and detection time were 90.05 and 95.23% and 3.7 and 2.2 s, respectively, on the two test datasets. Experimental results demonstrate that the proposed YOLOv3-DMRF is efficient for detection and recognition of thyroid nodules for ultrasound 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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Automated detection and recognition of thyroid nodules in ultrasound images using Improve Cascade Mask R-CNN

Multimedia Tools and Applications ◽

10.1007/s11042-021-10939-4 ◽

2021 ◽

Author(s):

Yinghao Zheng ◽

Lina Qin ◽

Taorong Qiu ◽

Aiyun Zhou ◽

Pan Xu ◽

...

Keyword(s):

Thyroid Nodules ◽

Automated Detection ◽

Ultrasound Images ◽

Detection And Recognition

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Comparative Analysis on Deep Learning Approaches for Heavy-Vehicle Detection based on Data Augmentation and Transfer-Learning techniques

Journal of Scientific Research ◽

10.3329/jsr.v13i3.52332 ◽

2021 ◽

Vol 13 (3) ◽

pp. 809-820

Author(s):

V. Sowmya ◽

R. Radha

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Real Time ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Traffic Management ◽

Data Augmentation ◽

Vehicle Detection ◽

Heavy Vehicles ◽

Detection And Recognition

Vehicle detection and recognition require demanding advanced computational intelligence and resources in a real-time traffic surveillance system for effective traffic management of all possible contingencies. One of the focus areas of deep intelligent systems is to facilitate vehicle detection and recognition techniques for robust traffic management of heavy vehicles. The following are such sophisticated mechanisms: Support Vector Machine (SVM), Convolutional Neural Networks (CNN), Regional Convolutional Neural Networks (R-CNN), You Only Look Once (YOLO) model, etcetera. Accordingly, it is pivotal to choose the precise algorithm for vehicle detection and recognition, which also addresses the real-time environment. In this study, a comparison of deep learning algorithms, such as the Faster R-CNN, YOLOv2, YOLOv3, and YOLOv4, are focused on diverse aspects of the features. Two entities for transport heavy vehicles, the buses and trucks, constitute detection and recognition elements in this proposed work. The mechanics of data augmentation and transfer-learning is implemented in the model; to build, execute, train, and test for detection and recognition to avoid over-fitting and improve speed and accuracy. Extensive empirical evaluation is conducted on two standard datasets such as COCO and PASCAL VOC 2007. Finally, comparative results and analyses are presented based on real-time.

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

Medical Physics ◽

10.1002/mp.15319 ◽

2021 ◽

Author(s):

Zijian Zhao ◽

Congmin Yang ◽

Qian Wang ◽

Huawei Zhang ◽

Linlin Shi ◽

...

Keyword(s):

Deep Learning ◽

Thyroid Nodules ◽

Ultrasound Images

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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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Deep learning radiomics for non-invasive diagnosis of benign and malignant thyroid nodules using ultrasound images

Medical Imaging 2020: Ultrasonic Imaging and Tomography ◽

10.1117/12.2549433 ◽

2020 ◽

Author(s):

Hui Zhou ◽

Kun Wang ◽

Jie Tian

Keyword(s):

Deep Learning ◽

Thyroid Nodules ◽

Ultrasound Images ◽

Non Invasive

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Vision-Based Classification of Mosquito Species: Comparison of Conventional and Deep Learning Methods

Applied Sciences ◽

10.3390/app9183935 ◽

2019 ◽

Vol 9 (18) ◽

pp. 3935 ◽

Cited By ~ 3

Author(s):

Kazushige Okayasu ◽

Kota Yoshida ◽

Masataka Fuchida ◽

Akio Nakamura

Keyword(s):

Deep Learning ◽

Conventional Method ◽

Data Augmentation ◽

Mosquito Species ◽

Experimental Results ◽

Support Vector ◽

Learning Method ◽

Species Classification ◽

Scale Invariant ◽

Machine Method

This study aims to propose a vision-based method to classify mosquito species. To investigate the efficiency of the method, we compared two different classification methods: The handcraft feature-based conventional method and the convolutional neural network-based deep learning method. For the conventional method, 12 types of features were adopted for handcraft feature extraction, while a support vector machine method was adopted for classification. For the deep learning method, three types of architectures were adopted for classification. We built a mosquito image dataset, which included 14,400 images with three types of mosquito species. The dataset comprised 12,000 images for training, 1500 images for testing, and 900 images for validating. Experimental results revealed that the accuracy of the conventional method using the scale-invariant feature transform algorithm was 82.4% at maximum, whereas the accuracy of the deep learning method was 95.5% in a residual network using data augmentation. From the experimental results, deep learning can be considered to be effective for classifying the mosquito species of the proposed dataset. Furthermore, data augmentation improves the accuracy of mosquito species’ classification.

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Thyroid Nodules Classification in Medical Ultrasound Images using Deep Learning

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.g5163.059720 ◽

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.

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Two Birds with One Stone: Series Saliency for Accurate and Interpretable Multivariate Time Series Forecasting

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/397 ◽

2021 ◽

Author(s):

Qingyi Pan ◽

Wenbo Hu ◽

Ning Chen

Keyword(s):

Time Series ◽

Deep Learning ◽

Data Augmentation ◽

Multivariate Time Series ◽

Time Series Forecasting ◽

Experimental Results ◽

Learning Methods ◽

Forecasting Accuracy ◽

Sliding Windows ◽

Scheme Of Series

It is important yet challenging to perform accurate and interpretable time series forecasting. Though deep learning methods can boost forecasting accuracy, they often sacrifice interpretability. In this paper, we present a new scheme of series saliency to boost both accuracy and interpretability. By extracting series images from sliding windows of the time series, we design series saliency as a mixup strategy with a learnable mask between the series images and their perturbed versions. Series saliency is model agnostic and performs as an adaptive data augmentation method for training deep models. Moreover, by slightly changing the objective, we optimize series saliency to find a mask for interpretable forecasting in both feature and time dimensions. Experimental results on several real datasets demonstrate that series saliency is effective to produce accurate time-series forecasting results as well as generate temporal interpretations.

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