Ultrasound Image Segmentation Algorithm of Thyroid Nodules Based on Improved U-Net Network

Gan, Jiulin; Zhang, Rongqiao

doi:10.1145/3562007.3562018

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Therefore, in order to further reduce the working pressure of staff and improve the efficiency and quality of diagnosis and treatment, computer aided detection technology should be built based on automatic detection of tuberculosis, so as to help them more efficient and accurate diagnosis and treatment. [1][2][3] At present, the integration of cutting-edge technologies in the field of artificial intelligence and medical image big data is the core content of research in the medical field. As an important representative of this kind of technology, deep learning can automatically learn the best features from a large amount of data, without manually designing complex features, and ensure the flexibility and versatility of learning features.…”

Section: Introductionmentioning

confidence: 99%

Performance study of automatic detection of pulmonary nodules based on convolutional neural network

Ren¹,

Huang²,

Liu³

2023

AETR

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In the medical field, the continuous monitoring of the probability of lung cancer incidence can be known that the actual growth rate is becoming faster and faster, and the complications become more and more serious. According to the research and analysis of experts in various fields, it is found that haze is one of the main factors leading to the occurrence of lung cancer in human body, especially in the urban environment. How to use the theory of artificial intelligence technology to design the automatic detection system of lung nodules is the core issue discussed in the field of medical science and technology at present. On the basis of understanding the status quo of convolutional neural network and lung cancer diagnosis and treatment, this study uses CAD system to complete the detection work from four steps, and combines practical cases to judge the detection performance of this technology, in order to lay a foundation for the development of technical innovation in the medical field.

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Section: Introductionmentioning

confidence: 99%

Performance study of automatic detection of pulmonary nodules based on convolutional neural network

Ren¹,

Huang²,

Liu³

2023

AETR

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“…Finally, the output of the decoder is sent to the soft-max classifier to generate class probability for each pixel independently, which improves the accuracy of image segmentation. Gan et al [25] proposed an improved Unet model of self attention mechanism, which had certain improvement in image segmentation of similar regions. Qian et al [26] improved the R-CNN network and introduced the characteristic pyramid network into the backbone network, which achieved higher accuracy in traffic sign segmentation.…”

Section: Introductionmentioning

confidence: 99%

Complex Traffic Scene Image Classification Based on Sparse Optimization Boundary Semantics Deep Learning

Zhou

Wang

et al. 2023

Wuhan Univ. J. Nat. Sci.

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With the rapid development of intelligent traffic information monitoring technology, accurate identification of vehicles, pedestrians and other objects on the road has become particularly important. Therefore, in order to improve the recognition and classification accuracy of image objects in complex traffic scenes, this paper proposes a segmentation method of semantic redefine segmentation using image boundary region. First, we use the SegNet semantic segmentation model to obtain the rough classification features of the vehicle road object, then use the simple linear iterative clustering (SLIC) algorithm to obtain the over segmented area of the image, which can determine the classification of each pixel in each super pixel area, and then optimize the target segmentation of the boundary and small areas in the vehicle road image. Finally, the edge recovery ability of condition random field (CRF) is used to refine the image boundary. The experimental results show that compared with FCN-8s and SegNet, the pixel accuracy of the proposed algorithm in this paper improves by 2.33% and 0.57%, respectively. And compared with Unet, the algorithm in this paper performs better when dealing with multi-target segmentation.

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Assessment of encoder-decoder-based segmentation models for thyroid ultrasound images

Yadav

Dass

Virmani

2023

Med Biol Eng Comput

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Ultrasound Image Segmentation Algorithm of Thyroid Nodules Based on Improved U-Net Network

Cited by 4 publications

References 5 publications

Performance study of automatic detection of pulmonary nodules based on convolutional neural network

Performance study of automatic detection of pulmonary nodules based on convolutional neural network

Complex Traffic Scene Image Classification Based on Sparse Optimization Boundary Semantics Deep Learning

Assessment of encoder-decoder-based segmentation models for thyroid ultrasound images

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