Recently, smog has reappeared, which is seriously affecting people's lives and endangering people's health. Under the influence of air pollution such as smog and PM2.5, the number of lung cancer patients in various countries and even the world has shown a significant upward trend. This paper aims to investigate lung cancer detection considering convolutional autoencoder neural networks. After studying the research status at home and abroad, this paper further discusses the existing problems and problems. For big data CT images, an automatic diagnosis model of benign lung nodules based on the established automatic neural network. Variants are saved as examples. Using samples to train a nonlinear network model to achieve objective diagnosis of benign and malignant pulmonary nodules. This method can improve the classification accuracy and classification speed of pulmonary nodules on the basis of avoiding complex algorithms such as feature extraction. By building a neural network model, this work reduces the complexity of the algorithm, improves the overall detection rate of pulmonary nodules, and reduces the false-positive rate and missed-diagnosis rate during testing and verification of large data samples. This provides doctors with an accurate, efficient and convenient way of diagnosis and has a positive effect on the early diagnosis and treatment of lung cancer.

Xuejiao Zi. Lung Cancer Detection Considering Convolutional Autoencoder Neural Network. International Journal of Neural Network (2020), Vol. 1, Issue 4: 18-25. https://doi.org/10.38007/NN.2020.010403.

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