دراسة مقارنة بين أداء الشبكات العصبية ونظام الاستدلال العصبي الضبابي المتكيف في تشخيص سرطان الثدي بالاعتماد على السمات البنيوية
Abstract
في هذه البحث تم تصميم شبكة عصبية اصطناعية تعتمد على خوارزمية الانتشار الخلفي للخطأ(BPNN) لتشخيص أورام الثدي وكذلك تصميم مصنف للتشخيص باستخدام نظام الاستدلال العصبي الضبابي المتكيف (ANFIS) وقد اعتمدت كلا الدراستين على السمات البنيوية للخزع الموجودة في قاعدة البيانات لصور الثدي لجامعة ويسكونسون في الولايات المتحدة الأميركية” Wisconson Brest Cancer dataset“
في النهاية تم اجراء مقارنة بين الدراستين من أجل التشخيص الحميد والخبيث للكتل السرطانية لسرطان الثدي حيث حصلت الدراسة الاولى BPNN على دقة %95.95 بينما الدراسة الثانية ANFIS حصلت على دقة91.9% وهذه النتائج تعتبر هامة جدا ومساعدة إذا ما قورنت بالأبحاث المعتمدة على السمات الشكلية المأخوذة من الصور لأجهزة متنوعة كالماموغراف والرنين المغناطيسي.
This research aims to produce a diagnosis system for breast cancer by using Neural Network depending on Back Propagation algorithm(BPNN) and Adaptive Neuro Fuzzy Inference System ‘ANFIS’, the both of studies was done using structural features of biopsies in “Wisconson Breast Cancer “data base.
In the end a comparison was made between the two studies of malignant- benign classification of breast masses of breast cancer which has accuracy 95,95% with BPNN and 91.9% with ANFIS system, this results can be consider very important if they compared with researches depending on image features that obtained of various devises like mammography, magnetic resonance.
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