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. 2022 Mar 26:2022:3972298.
doi: 10.1155/2022/3972298. eCollection 2022.

Models of Artificial Intelligence-Assisted Diagnosis of Lung Cancer Pathology Based on Deep Learning Algorithms

Su Chen  1
Affiliations

Models of Artificial Intelligence-Assisted Diagnosis of Lung Cancer Pathology Based on Deep Learning Algorithms

Su Chen. J Healthc Eng. .

Retraction in

Abstract

In this article, in order to explore the application of a diagnosis system for lung cancer, we use an auxiliary diagnostic system to predict and diagnose the good and evil attributes of chest CT pulmonary nodules. This research improves the new diagnosis method based on the convolutional neural network (CNN) and the recurrent neural network (RNN) and combines the dual effects of the two algorithms to process the classification of benign and malignant nodules. By collecting H-E-stained pathological slices of 652 patients' lung lesions from two hospitals between January 2018 and January 2019, the output results of the improved 3D U-net system and the consistent results of two-person reading were compared. This article analyzes the sensitivity, specificity, positive flammability rate, and negative flammability rate of different lung nodule detection methods. In addition, the artificial intelligence system's and the radiologist's judgment results of benign and malignant pulmonary nodules are used to draw ROC curves for further analysis. The improved model has an accuracy rate of 92.3% for predicting malignant lung nodules and an accuracy rate of 82.8% for benign lung nodules. The new diagnostic method using the convolutional neural network and the recurrent neural network can be very effective for improving the accuracy of predicting lung cancer diagnosis. It can play a very effective role in the disease prediction of lung cancer patients, thereby improving the treatment effect.

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Conflict of interest statement

The author declares that there are no conflicts of interest with any financial organizations regarding the material reported in this article.

Figures

Figure 1
Figure 1
CT image of lung nodules.
Figure 2
Figure 2
CT principle.
Figure 3
Figure 3
The first-generation neural network perception model.
Figure 4
Figure 4
RBM power model.
Figure 5
Figure 5
Convolutional neural network.
Figure 6
Figure 6
LSTM network structure diagram.
Figure 7
Figure 7
Improved 3D U-net model diagram.
Figure 8
Figure 8
Pathological gold standard, AI model, and pathological regions segmented by pathologists (black line: AI model, gray line: gold standard, and white line: pathologist).
Figure 9
Figure 9
ROC curve of artificial intelligence reading and physician reading.
Figure 10
Figure 10
ROC curve of diameter, CT value, and malignant probability.
See this image and copyright information in PMC

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