Classification of Malaria-Infected Cells Using Deep Convolutional Neural Networks

  • Pan W
  • Dong Y
  • Wu D
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Abstract

Background: Cancer patients are increasingly seeking out complementary and alternative medicine (CAM) and might be reluctant to disclose its use to their oncology treatment team. Often, CAM agents are not well studied, and little is known about their potential interactions with chemotherapy, radiation therapy, or biologic therapies, and their correlations with outcomes. In the present study, we set out to determine the rate of CAM use in patients receiving treatment at a Northern Ontario cancer centre. Methods: Patients reporting for treatment at the Northeast Cancer Centre (NECC) in Sudbury, Ontario, were asked to complete an anonymous questionnaire to assess CAM use. Changes in CAM use before, compared with after, diagnosis were also assessed. Results: Patients in Northern Ontario reported significant CAM use both before and after diagnosis. However, as a function of the CAM type, CAM use was greatly enhanced after cancer diagnosis. For example, the number of patients who reported use of biologic products increased to 51.8% after a cancer diagnosis from 15.6% before a cancer diagnosis. Patients reported much smaller changes in the use of alternative medical systems or spiritual therapy after diagnosis. Vitamin use was reported by 66% of respondents, and the number of different CAMS used correlated significantly with the reported number of vitamins used. Conclusions: Use of CAM, particularly biologic products, increased significantly after a cancer diagnosis. Further studies are required to examine the effect of CAM use on the efficacy and safety of cancer therapies.

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APA

Pan, W. D., Dong, Y., & Wu, D. (2018). Classification of Malaria-Infected Cells Using Deep Convolutional Neural Networks. In Machine Learning - Advanced Techniques and Emerging Applications. InTech. https://doi.org/10.5772/intechopen.72426

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