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      Retracted: Deep Learning-Based Chest CT Image Features in Diagnosis of Lung Cancer

      retraction
      Computational and Mathematical Methods in Medicine
      Hindawi

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          Abstract

          This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation The presence of these indicators undermines our confidence in the integrity of the article's content and we cannot, therefore, vouch for its reliability. Please note that this notice is intended solely to alert readers that the content of this article is unreliable. We have not investigated whether authors were aware of or involved in the systematic manipulation of the publication process. Wiley and Hindawi regrets that the usual quality checks did not identify these issues before publication and have since put additional measures in place to safeguard research integrity. We wish to credit our own Research Integrity and Research Publishing teams and anonymous and named external researchers and research integrity experts for contributing to this investigation. The corresponding author, as the representative of all authors, has been given the opportunity to register their agreement or disagreement to this retraction. We have kept a record of any response received.

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          Deep Learning-Based Chest CT Image Features in Diagnosis of Lung Cancer

          This study was to evaluate the diagnostic value of deep learning-optimized chest CT in the patients with lung cancer. 90 patients who were diagnosed with lung cancer by surgery or puncture in hospital were selected as the research subjects. The Mask Region Convolutional Neural Network (Mask-RCNN) model was a typical end-to-end image segmentation model, and Dual Path Network (DPN) was used in nodule detection. The results showed that the accuracy of DPN algorithm model in detecting lung lesions in lung cancer patients was 88.74%, the accuracy of CT diagnosis of lung cancer was 88.37%, the sensitivity was 82.91%, and the specificity was 87.43%. Deep learning-based CT examination combined with serum tumor detection, factoring into Neurospecific enolase (N S E), cytokeratin 19 fragment (CYFRA21), Carcinoembryonic antigen (CEA), and squamous cell carcinoma (SCC) antigen, improved the accuracy to 97.94%, the sensitivity to 98.12%, and the specificity to 100%, all showing significant differences (P < 0.05). In conclusion, this study provides a scientific basis for improving the diagnostic efficiency of CT imaging in lung cancer and theoretical support for subsequent lung cancer diagnosis and treatment.
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            Author and article information

            Contributors
            Journal
            Comput Math Methods Med
            Comput Math Methods Med
            cmmm
            Computational and Mathematical Methods in Medicine
            Hindawi
            1748-670X
            1748-6718
            2023
            1 November 2023
            1 November 2023
            : 2023
            : 9817864
            Affiliations
            Article
            10.1155/2023/9817864
            10631910
            20a73f52-98a6-4ee8-97bf-147174850567
            Copyright © 2023 Computational and Mathematical Methods in Medicine.

            This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

            History
            : 31 October 2023
            : 31 October 2023
            Categories
            Retraction

            Applied mathematics
            Applied mathematics

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