Ensemble Deep Learning Model for Multicenter Classification of Thyroid Nodules on Ultrasound Images

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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Sea Ice and Open Water Classification of SAR Images Using a Deep Learning Model

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9323990 ◽

2020 ◽

Author(s):

Yibin Ren ◽

Huan Xu ◽

Bin Liu ◽

Xiaofeng Li

Keyword(s):

Deep Learning ◽

Sea Ice ◽

Open Water ◽

Learning Model ◽

Sar Images ◽

Water Classification ◽

Deep Learning Model

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Automatic Prediction and Classification of Diseases in Melons using Stacked RNN based Deep Learning Model

2020 International Conference on System, Computation, Automation and Networking (ICSCAN) ◽

10.1109/icscan49426.2020.9262414 ◽

2020 ◽

Author(s):

D. Jayakumar ◽

A. Elakkiya ◽

R. Rajmohan ◽

M.O. Ramkumar

Keyword(s):

Deep Learning ◽

Learning Model ◽

Classification Of Diseases ◽

Deep Learning Model

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Detection and classification of arrhythmia using an explainable deep learning model

Journal of Electrocardiology ◽

10.1016/j.jelectrocard.2021.06.006 ◽

2021 ◽

Author(s):

Yong-Yeon Jo ◽

Joon-myoung Kwon ◽

Ki-Hyun Jeon ◽

Yong-Hyeon Cho ◽

Jae-Hyun Shin ◽

...

Keyword(s):

Deep Learning ◽

Learning Model ◽

Deep Learning Model

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A Deep Learning Model Based on Convolutional Neural Networks for Classification of Magnetic Resonance Prostate Images

Artificial Intelligence and Applied Mathematics in Engineering Problems - Lecture Notes on Data Engineering and Communications Technologies ◽

10.1007/978-3-030-36178-5_59 ◽

2020 ◽

pp. 701-708

Author(s):

Fatih Uysal ◽

Fırat Hardalaç ◽

Mustafa Koç

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Magnetic Resonance ◽

Convolutional Neural Networks ◽

Learning Model ◽

Model Based ◽

Deep Learning Model

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Deep Learning Model for Classification of Breast Cancer

Artificial Intelligence in Breast Cancer Early Detection and Diagnosis ◽

10.1007/978-3-030-59208-0_6 ◽

2020 ◽

pp. 93-100

Author(s):

Khalid Shaikh ◽

Sabitha Krishnan ◽

Rohit Thanki

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Learning Model ◽

Deep Learning Model

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Attention-Guided Digital Adversarial Patches on Visual Detection

Security and Communication Networks ◽

10.1155/2021/6637936 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Dapeng Lang ◽

Deyun Chen ◽

Ran Shi ◽

Yongjun He

Keyword(s):

Deep Learning ◽

Learning Model ◽

Detection Algorithm ◽

Classification Model ◽

Practical Application ◽

Single Target ◽

Attachment Mechanism ◽

Human Eyes ◽

Deep Learning Model

Deep learning has been widely used in the field of image classification and image recognition and achieved positive practical results. However, in recent years, a number of studies have found that the accuracy of deep learning model based on classification greatly drops when making only subtle changes to the original examples, thus realizing the attack on the deep learning model. The main methods are as follows: adjust the pixels of attack examples invisible to human eyes and induce deep learning model to make the wrong classification; by adding an adversarial patch on the detection target, guide and deceive the classification model to make it misclassification. Therefore, these methods have strong randomness and are of very limited use in practical application. Different from the previous perturbation to traffic signs, our paper proposes a method that is able to successfully hide and misclassify vehicles in complex contexts. This method takes into account the complex real scenarios and can perturb with the pictures taken by a camera and mobile phone so that the detector based on deep learning model cannot detect the vehicle or misclassification. In order to improve the robustness, the position and size of the adversarial patch are adjusted according to different detection models by introducing the attachment mechanism. Through the test of different detectors, the patch generated in the single target detection algorithm can also attack other detectors and do well in transferability. Based on the experimental part of this paper, the proposed algorithm is able to significantly lower the accuracy of the detector. Affected by the real world, such as distance, light, angles, resolution, etc., the false classification of the target is realized by reducing the confidence level and background of the target, which greatly perturbs the detection results of the target detector. In COCO Dataset 2017, it reveals that the success rate of this algorithm reaches 88.7%.

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