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A Deep Belief Network and Dempster-Shafer-Based Multiclassifier for the Pathology Stage of Prostate Cancer
المؤلفون المشاركون
Lee, Ji Youl
Byun, Seok-Soo
Jeong, Chang Wook
Koo, Kyo Chul
Kim, Choung-Soo
Seo, Seong Il
Kim, Jae Kwon
Choi, Mun Joo
Lee, Jong Sik
Hong, Jun Hyuk
Chung, Byung Ha
Park, Yong Hyun
Choi, In Young
المصدر
Journal of Healthcare Engineering
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-8، 8ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-03-19
دولة النشر
مصر
عدد الصفحات
8
التخصصات الرئيسية
الملخص EN
Object.
Pathologic prediction of prostate cancer can be made by predicting the patient’s prostate metastasis prior to surgery based on biopsy information.
Because biopsy variables associated with pathology have uncertainty regarding individual patient differences, a method for classification according to these variables is needed.
Method.
We propose a deep belief network and Dempster-Shafer- (DBN-DS-) based multiclassifier for the pathologic prediction of prostate cancer.
The DBN-DS learns prostate-specific antigen (PSA), Gleason score, and clinical T stage variable information using three DBNs.
Uncertainty regarding the predicted output was removed from the DBN and combined with information from DS to make a correct decision.
Result.
The new method was validated on pathology data from 6342 patients with prostate cancer.
The pathology stages consisted of organ-confined disease (OCD; 3892 patients) and non-organ-confined disease (NOCD; 2453 patients).
The results showed that the accuracy of the proposed DBN-DS was 81.27%, which is higher than the 64.14% of the Partin table.
Conclusion.
The proposed DBN-DS is more effective than other methods in predicting pathology stage.
The performance is high because of the linear combination using the results of pathology-related features.
The proposed method may be effective in decision support for prostate cancer treatment.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Kim, Jae Kwon& Choi, Mun Joo& Lee, Jong Sik& Hong, Jun Hyuk& Kim, Choung-Soo& Seo, Seong Il…[et al.]. 2018. A Deep Belief Network and Dempster-Shafer-Based Multiclassifier for the Pathology Stage of Prostate Cancer. Journal of Healthcare Engineering،Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1187289
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Kim, Jae Kwon…[et al.]. A Deep Belief Network and Dempster-Shafer-Based Multiclassifier for the Pathology Stage of Prostate Cancer. Journal of Healthcare Engineering No. 2018 (2018), pp.1-8.
https://search.emarefa.net/detail/BIM-1187289
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Kim, Jae Kwon& Choi, Mun Joo& Lee, Jong Sik& Hong, Jun Hyuk& Kim, Choung-Soo& Seo, Seong Il…[et al.]. A Deep Belief Network and Dempster-Shafer-Based Multiclassifier for the Pathology Stage of Prostate Cancer. Journal of Healthcare Engineering. 2018. Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1187289
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1187289
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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