Abstract
An esophageal cancer intelligent diagnosis system is developed to improve the recognition rate of esophageal cancer image diagnosis and the efficiency of physicians, as well as to improve the level of esophageal cancer image diagnosis in primary care institutions. In this paper, by collecting medical images related to esophageal cancer over the years, we establish an intelligent diagnosis system based on the convolutional neural network for esophageal cancer images through the steps of data annotation, image preprocessing, data enhancement, and deep learning to assist doctors in intelligent diagnosis. The convolutional neural network-based esophageal cancer image intelligent diagnosis system has been successfully applied in hospitals and widely praised by frontline doctors. This system is beneficial for primary care physicians to improve the overall accuracy of esophageal cancer diagnosis and reduce the risk of death of esophageal cancer patients. We also analyze that the efficacy of radiation therapy for esophageal cancer can be influenced by many factors, and clinical attention should be paid to grasp the relevant factors in order to improve the final treatment effect and prognosis of patients.
Highlights
Esophageal cancer has a high incidence in clinical practice, and its occurrence can have a serious impact on patients’ health, so radiation therapy is often used in clinical practice to achieve good therapeutic effects; in the treatment of elderly patients with esophageal cancer, the effect of radiation therapy is often not very satisfactory, so we consider that it may be influenced by different factors, and we analyze the possible influencing factors with the purpose to summarize the effective experience for clinical reference.Esophageal cancer is a common tumor of the digestive tract, and about 390,000 people die from esophageal cancer worldwide every year
Taking early screening of esophageal cancer as the research object, through joint research with Tencent and other outstanding companies, we have developed an intelligent diagnosis system for esophageal cancer with convolutional neural network technology
In order to verify the robustness of ODSNet for organthreatening segmentation in patients with different T-stages of Yi-pharyngeal cancer, we used the analysis of variance (ANOVA) to compare the DSC values of organ-threatening segmentation in patients with different T-stages, and the results are shown in Table 3. e DSCs of each endangered organ did not differ significantly among patients with different T-stages, which indicated that ODSNet was robust to ODS net (a)
Summary
Esophageal cancer has a high incidence in clinical practice, and its occurrence can have a serious impact on patients’ health, so radiation therapy is often used in clinical practice to achieve good therapeutic effects; in the treatment of elderly patients with esophageal cancer, the effect of radiation therapy is often not very satisfactory, so we consider that it may be influenced by different factors, and we analyze the possible influencing factors with the purpose to summarize the effective experience for clinical reference. China is one of the regions with high incidence of esophageal cancer in the world, and esophageal cancer is one of the top five malignant tumors in China, with an average of about 150,000 disease deaths per year. More than 90% of medical data comes from medical images, and the current mainstream diagnostic method of medical images relies on manual analysis of these images [2], which has two obvious shortcomings: first, it is imprecise, and the recognition accuracy is completely dependent on the experience of diagnosing physicians, which is easy to cause misjudgment; second, China’s medical image growth rate is about 30% per year, while the annual growth rate of the number of radiologists is about 4%, and the growth of the number of radiologists is far less than the growth of imaging data [3]. More than 90% of medical data comes from medical images, and the current mainstream diagnostic method of medical images relies on manual analysis of these images [2], which has two obvious shortcomings: first, it is imprecise, and the recognition accuracy is completely dependent on the experience of diagnosing physicians, which is easy to cause misjudgment; second, China’s medical image growth rate is about 30% per year, while the annual growth rate of the number of radiologists is about 4%, and the growth of the number of radiologists is far less than the growth of imaging data [3]. erefore, the use of new technical means to solve medical image analysis has become an urgent problem
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