Abstract
The case characteristics of lung cancer are extremely complex, difficult to distinguish, and the rate of deterioration is rapid, and its early symptoms are not obvious. Early diagnosis and treatment of lung cancer is one of the main directions to reduce lung cancer mortality. The computer-aided detection and diagnosis system reduces the workload of the physician and improves the accuracy of image reading. In this paper, based on the analysis of the current lung nodule segmentation algorithm, in order to enhance the accuracy of lung nodule segmentation extraction, the adaptive particle swarm optimization algorithm is used to realize the simultaneous optimization of the number of mixed components and the model parameters, and finally realize the segmentation of lung nodules. The effectiveness and accuracy of segmentation of lung nodule recognition by adaptive particle swarm optimization algorithm is verified by adaptive particle swarm optimization and image model establishment. Provide new aids for the identification of pulmonary nodules.
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Wang, Y., Ding, J., Fang, W., Cao, J. (2019). Segmentation-Assisted Diagnosis of Pulmonary Nodule Recognition Based on Adaptive Particle Swarm Image Algorithm. In: Ning, H. (eds) Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health. CyberDI CyberLife 2019 2019. Communications in Computer and Information Science, vol 1138. Springer, Singapore. https://doi.org/10.1007/978-981-15-1925-3_36
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DOI: https://doi.org/10.1007/978-981-15-1925-3_36
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