A New Pulmonary Nodule Detection Based on Multiscale Convolutional Neural Network with Channel and Attention Mechanism

Zhao, Yingying; Wang, Jiaxin; Wang, Xiaomin; Wan, Honglin

doi:10.1007/978-981-19-3387-5_120

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…They reported a sensitivity of 0.920, and an average freeresponse ROC of 0.862 was achieved using the LUNA16 dataset. Zhao et al [7] used ResNet as the encoder and U-Net as the decoder. The authors added some multi-scale feature extraction and attention mechanism blocks to certain residual blocks in the ResNet to make the network perform better.…”

Section: Related Workmentioning

confidence: 99%

Study of a Deep Convolution Network with Enhanced Region Proposal Network in the Detection of Cancerous Lung Tumors

Lee,

Hsu,

Chien

2024

Bioengineering

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A deep convolution network that expands on the architecture of the faster R-CNN network is proposed. The expansion includes adapting unsupervised classification with multiple backbone networks to improve the Region Proposal Network in order to improve accuracy and sensitivity in detecting minute changes in images. The efficiency of the proposed architecture is investigated by applying it to the detection of cancerous lung tumors in CT (computed tomography) images. This investigation used a total of 888 images from the LUNA16 dataset, which contains CT images of both cancerous and non-cancerous tumors of various sizes. These images are divided into 80% and 20%, which are used for training and testing, respectively. The result of the investigation through the experiment is that the proposed deep-learning architecture could achieve an accuracy rate of 95.32%, a precision rate of 94.63%, a specificity of 94.84%, and a high sensitivity of 96.23% using the LUNA16 images. The result shows an improvement compared to a reported accuracy of 93.6% from a previous study using the same dataset.

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Section: Related Workmentioning

confidence: 99%

Study of a Deep Convolution Network with Enhanced Region Proposal Network in the Detection of Cancerous Lung Tumors

Lee,

Hsu,

Chien

2024

Bioengineering

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“…To be specific, channel attention was employed to capture relationships between channels of the feature map to downplay meaningless channels that are not relevant to the lesion, while spatial attention was utilized to motivate the network to pay attention to regions that are more relevant to the lesion. The multi-scale contextual information fusion cascade hybrid attention model was presented by Pan et al [79] for the detection of nasopharyngeal lesions. Among them, the cascaded hybrid space and channel attention modules aim to transfer attention between the convolutional blocks of the front and back cascades.…”

Section: Hybrid Attention In Medical Image Detection Taskmentioning

confidence: 99%

Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds

Li²,

Yan³

et al. 2023

IJNDI

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Survey/review study Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds Xiang Li 1, Minglei Li 1, Pengfei Yan 1, Guanyi Li 1, Yuchen Jiang 1, Hao Luo 1,*, and Shen Yin 2 1 Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China 2 Department of Mechanical and Industrial Engineering, Faculty of Engineering, Norwegian University of Science and Technology, Trondheim 7034, Norway * Correspondence: hao.luo@hit.edu.cn Received: 16 October 2022 Accepted: 25 November 2022 Published: Abstract: With the improvement of hardware computing power and the development of deep learning algorithms, a revolution of "artificial intelligence (AI) + medical image" is taking place. Benefiting from diversified modern medical measurement equipment, a large number of medical images will be produced in the clinical process. These images improve the diagnostic accuracy of doctors, but also increase the labor burden of doctors. Deep learning technology is expected to realize an auxiliary diagnosis and improve diagnostic efficiency. At present, the method of deep learning technology combined with attention mechanism is a research hotspot and has achieved state-of-the-art results in many medical image tasks. This paper reviews the deep learning attention methods in medical image analysis. A comprehensive literature survey is first conducted to analyze the keywords and literature. Then, we introduce the development and technical characteristics of the attention mechanism. For its application in medical image analysis, we summarize the related methods in medical image classification, segmentation, detection, and enhancement. The remaining challenges, potential solutions, and future research directions are also discussed.

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“…full play to its corresponding value in the medical field as long as technical research and training of professionals are strengthened. After designing the CAD system, this paper mainly analyzed from the four aspects of lung parenchyma segmentation, extraction of areas of interest, computational features and detection of lung nodules, and verified it with practical cases, so as to clarify the application performance of the automatic detection technology of lung nodules with convolutional neural network as the core [4][5][6]. …”

mentioning

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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A New Pulmonary Nodule Detection Based on Multiscale Convolutional Neural Network with Channel and Attention Mechanism

Cited by 3 publications

References 8 publications

Study of a Deep Convolution Network with Enhanced Region Proposal Network in the Detection of Cancerous Lung Tumors

Study of a Deep Convolution Network with Enhanced Region Proposal Network in the Detection of Cancerous Lung Tumors

Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds

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

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