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Imaging Examination and Quantitative Detection and Analysis of Gastrointestinal Diseases Based on Data Mining Technology

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

The medical image storage and transmission system completes the collection, storage, management, diagnosis and information processing of digital medical image information generated from digital medical devices, accumulates a large amount of data resources, and uses these valuable data resources to extract corresponding diseases. Diagnostic rules that help improve the accuracy of clinical disease diagnosis have always been the subject of medical research and management. Gastrointestinal diseases are common high-risk digestive diseases. This paper studies the imaging detection and quantitative detection and analysis of gastrointestinal diseases based on data mining, aiming to improve the accuracy of doctors’ clinical diagnosis, reduce the misdiagnosis and misdiagnosis of patients’ diseases, and reduce the burden on patients. With the high computing speed and computational accuracy of the computer, combined with the flexible analysis and judgment ability of the human body, the doctor can help the semi-structured and unstructured diagnosis problems. Experiments demonstrate the effectiveness and robustness of the proposed method.

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References

  1. Chen, J., Yin, J., Hou, X. et al., Complementary and alternative therapies for functional gastrointestinal diseases 2016.[J]. Evidence-Based Complementary and Alternative Medicine 2017:1–3, 2017.

    Google Scholar 

  2. Dossett, M. L., Cohen, E. M., and Cohen, J., Integrative medicine for gastrointestinal disease.[J]. Primary Care Clinics in Office Practice 44(2):265–280, 2017.

    Article  Google Scholar 

  3. Sheedy, S. P., Th, E. F., Fletcher, J. G. et al., CT of small-bowel ischemia associated with obstruction in emergency department patients: Diagnostic performance evaluation.[J]. Radiol 241(3):729–736, 2017.

    Article  Google Scholar 

  4. Stone, D. E., and Quiroz, L. H., Ultrasound imaging of the pelvic floor [J]. Obstetrics & Gynecology Clinics of North America 43(1):141–153, 2016.

    Article  Google Scholar 

  5. Cai, K., Haris, M., Singh, A. et al., Magnetic resonance imaging of glutamate [J]. Radiology of Infectious Diseases 3(2):92–97, 2016.

    Article  Google Scholar 

  6. Jain, A. S., Shelley, S., Muthukrishnan, I., Kalal, S., Amalachandran, J., and Chandran, S., Diagnostic importance of contrast enhanced 18F-fluorodeoxyglucose positron emission computed tomography in patients with tumor induced osteomalacia: Our experience [J]. Indian J Nucl Med 31(1):14–19, 2016.

    Article  Google Scholar 

  7. Mu, Y. J., Feng, Y. C., and Gastroenterology, D. O., Significance of serum gastrin 17 in diagnosis of gastrointestinal diseases [J]. World Chinese Journal of Digestology 24(19):2996, 2016.

    Article  CAS  Google Scholar 

  8. Feng B W, Fu S M, Zhang Q S, et al. [Influence of cow's milk protein allergy on the diagnosis of functional gastrointestinal diseases based on the Rome IV standard in infants and young children][J]. Chinese Journal of Contemporary Pediatrics, 2018, 20(1):56.

  9. Garciahernandez, J. J., Gomezflores, W., and Rubioloyola, J., Analysis of the impact of digital watermarking on computer-aided diagnosis in medical imaging.[J]. Computers in Biology & Medicine 68:37–48, 2016.

    Article  Google Scholar 

  10. Hermans, J. A. J., and Engelbertink, G. A. P., High-velocity DSA measurements in the 1–20 ps lifetime region: Comparison between DSA and RD [J]. Nuclear Physics 284(2):307–328, 2016.

    Article  Google Scholar 

  11. Cheng, J. Z., Ni, D., Chou, Y. H. et al., Computer-aided diagnosis with deep learning architecture: Applications to breast lesions in US images and pulmonary nodules in CT scans [J]. Scientific Reports 6:24454, 2016.

    Article  CAS  Google Scholar 

  12. Lei, Y., Hu, P., Li, C. et al., The performance prediction of ground source heat pump system based on monitoring data and data mining technology [J]. Energy & Buildings 127:1085–1095, 2016.

    Article  Google Scholar 

  13. Pan, H., Wang, H., Wang, Y. et al., [Rules of acupoint selection for diabetic peripheral neuropathy based on data mining technology].[J]. Zhongguo Zhen Jiu:1111–1114, 2016.

  14. Masten Y, Ashcraft A. Due diligence in the open-access explosion era: choosing a reputable journal for publication:[J]. Fems Microbiology Letters, 2017, 364(21).

  15. Jin, M., Wang, Y., and Zeng, Y., Application of data Mining Technology in Financial Risk Analysis [J]. Wireless Personal Communications 1:1–15, 2018.

    Google Scholar 

  16. Wei, W., Su, J., Song, H. et al., CDMA-based anti-collision algorithm for EPC global C1 Gen2 systems [J]. Telecommunication Systems 67(1):63–71, 2018.

    Article  Google Scholar 

  17. Wei, W., Woźniak, M., Damaševičius, R. et al., Algorithm research of known-plaintext attack on double random phase mask based on WSNs [J]. Journal of Internet Technology 20(1):39–48, 2019.

    Google Scholar 

  18. Shafiee, S., and Minaei, S., Combined data mining /NIR spectroscopy for purity assessment of lime juice [J]. Infrared Physics & Technology 91:193–199, 2018.

    Article  CAS  Google Scholar 

  19. Rubiano, S. M. M., and Garcia, J. A. D., Analysis of data mining techniques for constructing a predictive model for academic performance [J]. IEEE Latin America Transactions 14(6):2783–2788, 2016.

    Article  Google Scholar 

  20. Amani, F. A., and Fadlalla, A. M., Data mining applications in accounting: A review of the literature and organizing framework [J]. International Journal of Accounting Information Systems 24:32–58, 2017.

    Article  Google Scholar 

  21. Yue, Z., Guo, S. L., Han, L. N. et al., Application and exploration of big data Mining in Clinical Medicine:[J]. Chinese Medical Journal 129(6):731–738, 2016.

    Article  Google Scholar 

  22. Tzanis, G., Biological and medical big data mining.[J]. International Journal of Knowledge Discovery in Bioinformatics 4(1):42–56, 2017.

    Article  Google Scholar 

  23. Morotti, S., and Grandi, E., Logistic regression analysis of populations of electrophysiological models to assess proarrythmic risk [J]. Methodsx 4(C):25–34, 2017.

    Article  Google Scholar 

  24. Dong, L., Wesseloo, J., Potvin, Y. et al., Discrimination of mine seismic events and blasts using the fisher classifier, naive Bayesian classifier and logistic regression [J]. Rock Mechanics & Rock Engineering 49(1):183–211, 2016.

    Article  Google Scholar 

  25. Kharya, S., and Soni, S., Weighted naive Bayes classifier: A predictive model for breast Cancer detection [J]. International Journal of Computer Applications 133(9):32–37, 2016.

    Article  Google Scholar 

  26. Leema, N., Nehemiah, H. K., and Kannan, A., Neural network classifier optimization using differential evolution with global information and Back propagation algorithm for clinical datasets [J]. Applied Soft Computing 49:834–844, 2016.

    Article  Google Scholar 

  27. Hameed, A. A., Karlik, B., and Salman, M. S., Back-propagation algorithm with variable adaptive momentum [J]. Knowledge-Based Systems 114:79–87, 2016.

    Article  Google Scholar 

  28. Cui, K., and Zhao, T. T., Unsaturated dynamic constitutive model under cyclic loading [J]. Cluster Computing 20(4):2869–2879, 2017.

    Article  Google Scholar 

  29. Cui K, Jing X. Research on prediction model of geotechnical parameters based on BP neural network [J]. Neural Computing and Applications, 2018: 1–11.

  30. Wei, W., Zhou, B., Połap, D. et al., A regional adaptive variational PDE model for computed tomography image reconstruction [J]. Pattern Recognition 92:64–81, 2019.

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, 545005, Ghuangxi, China

    Tao Li

  2. First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Ghuangxi, China

    Liling Long

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  1. Tao Li
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  2. Liling Long
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Correspondence to Liling Long.

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This article is part of the Topical Collection on Image & Signal Processing

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Li, T., Long, L. Imaging Examination and Quantitative Detection and Analysis of Gastrointestinal Diseases Based on Data Mining Technology . J Med Syst 44, 31 (2020). https://doi.org/10.1007/s10916-019-1482-3

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  • DOI: https://doi.org/10.1007/s10916-019-1482-3

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