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Register a new userA Comparative Study between Artificial Neural Network Performance and Adaptive Neuro-fuzzy Inference Systems in Breast Cancer Diagnosis Depending On Structural Features
دراسة مقارنة بين أداء الشبكات العصبية و نظام الاستدلال العصبي الضبابي المتكيف في تشخيص سرطان الثدي بالاعتماد على السمات البنيوية
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بلسم أحمد عيد( باحث )
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في هذه البحث تم تصميم شبكة عصبية اصطناعية تعتمد على خوارزمية الانتشار الخلفي للخطأ (BPNN) لتشخيص أورام الثدي و كذلك تصميم مصنف للتشخيص باستخدام نظام الاستدلال العصبي الضبابي المتكيف (ANFIS) و قد 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 و الخبيث للكتل السرطانية لسرطان الثدي حيث حصلت الدراسة الاولى BPNN على دقة %95.95 بينما الدراسة الثانية ANFIS حصلت على دقة 91.9% و هذه النتائج تعتبر هامة جدا و مساعدة إذا ما قورنت بالأبحاث المعتمدة على السمات الشكلية المأخوذة من الصور لأجهزة متنوعة كالماموغراف و الرنين 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.
Artificial intelligence review:
Research summary
تقدم هذه الدراسة مقارنة بين أداء الشبكات العصبية الاصطناعية (BPNN) ونظام الاستدلال العصبي الضبابي المتكيف (ANFIS) في تشخيص سرطان الثدي. اعتمدت الدراسة على السمات البنيوية للخزع الموجودة في قاعدة بيانات جامعة ويسكونسن لسرطان الثدي. تم تصميم شبكة عصبية باستخدام خوارزمية الانتشار الخلفي للخطأ (BPNN) ونظام ANFIS لتصنيف الكتل السرطانية إلى حميدة وخبيثة. أظهرت النتائج أن الشبكة العصبية BPNN حققت دقة بنسبة 95.95% بينما حقق نظام ANFIS دقة بنسبة 91.9%. تعتبر هذه النتائج مهمة مقارنة بالأبحاث التي تعتمد على السمات الشكلية المستخرجة من الصور الطبية مثل الماموغرافيا والرنين المغناطيسي. تهدف الدراسة إلى تحسين دقة التشخيص ودعم الأطباء في اتخاذ قرارات علاجية مناسبة.
Critical review
تعتبر الدراسة ذات أهمية كبيرة في مجال تشخيص سرطان الثدي باستخدام تقنيات الذكاء الاصطناعي. ومع ذلك، يمكن الإشارة إلى بعض النقاط التي قد تحتاج إلى تحسين. أولاً، لم يتم توضيح كيفية اختيار السمات البنيوية المستخدمة في التصنيف بشكل كافٍ، مما قد يؤثر على دقة النتائج. ثانياً، لم يتم مناقشة تأثير حجم العينة على أداء المصنفين بشكل مفصل، وهو عامل مهم يجب مراعاته. أخيراً، يمكن تحسين أداء نظام ANFIS من خلال تحسين خوارزمياته واختيار توابع العضوية بشكل أكثر دقة بدلاً من الاعتماد على التجريب.
Questions related to the research
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ما هي دقة الشبكة العصبية الاصطناعية (BPNN) في تشخيص سرطان الثدي؟
حققت الشبكة العصبية الاصطناعية (BPNN) دقة بنسبة 95.95% في تشخيص سرطان الثدي.
-
ما هي السمات البنيوية التي اعتمدت عليها الدراسة في تصنيف الكتل السرطانية؟
اعتمدت الدراسة على السمات البنيوية التالية: سماكة الأجمة، انتظام حجم الخلية، انتظام شكل الخلية، نسبة الالتصاق الهامشي، حجم ظهارة الخلية الإفرادي، ونسبة تعري نواة الخلية.
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كيف يمكن تحسين أداء نظام ANFIS في تشخيص سرطان الثدي؟
يمكن تحسين أداء نظام ANFIS من خلال تحسين خوارزمياته واختيار توابع العضوية بشكل أكثر دقة بدلاً من الاعتماد على التجريب.
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ما هي أهمية هذه الدراسة في مجال تشخيص سرطان الثدي؟
تعتبر هذه الدراسة مهمة لأنها تقدم تقنيات جديدة لتحسين دقة تشخيص سرطان الثدي ودعم الأطباء في اتخاذ قرارات علاجية مناسبة، مما يمكن أن يقلل من معدلات الوفيات الناتجة عن التأخر في الكشف عن المرض.
References used
Ebrahim Edriss Ebrahim Ali, Wu Zhi Feng. Breast Cancer Classification using Support Vector Machine and Neural Network. International Journal of Science and Research (IJSR). Vol.5 No. 3, 2016, 1-6
K. A. Mohamed Junaid. Classification Using Two Layer Neural Network Back Propagation Algorithm. Circuits and Systems, Vol.1, No.7, 2016, 1207-1212
Htet Thazin, Tike Thein, Khin Mo. AN APPROACH FOR BREAST CANCER DIAGNOSIS CLASSIFICATION USING NEURAL NETWORK. Advanced Computing: An International Journal (ACIJ), Vol.6, No.1, 2015, 1-11
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