تصميم موديل حديث لمواجهة جائحة كوفيد-19 بناءا على تصميم موديل حديث لمواجهة جائحة كوفيد-19 بناءا على نموذج SVM و CNNSVM و CNN
DOI:
https://doi.org/10.21123/bsj.2023.7403الكلمات المفتاحية:
. RNN،تعلم الالة، التصوير الطبي، COVID-19، CNNالملخص
اثناء الازمة الصحية العالمية الناجمة عن فيروس كورونا (كوفيد-19), بدأ الباحثون والخبراء الطبيون بالبحث عن طرائق جديدة لمحاربة الوباء. وفقا للدراسات الحديثة فقد استخدم التعلم الالي بشكل فعال في القطاع الطبي. لقد ساعدت مصادر التصوير الطبي منها (التصوير الشعاعي والتصوير المقطعي المحوسب) في تطوير استراتيجيات الذكاء الاصطناعي من اجل التصدي لانتشار فيروس كورونا. ونتيجة لذلك ، تم تطوير نموذج تعلم آلي كلاسيكي لاكتشاف فيروس كورونا من صور التصوير المقطعي المحوسب (CT). تم اقتراح نموذج الشبكة العصبيه التلافيفية(CNN) والة ناقلات الدعم(SVM) لتصنيف التصوير المقطعي المحوري للرئة الى مجموعتين في هذه الدراسة (COVID-19,NonCOVID-19) كانت مجموعة البيانات المستخدمة في هذه الدراسة هي 960 شريحة من الأشعة المقطعية تم جمعها من المرضى العراقيين / مستشفى ابن النفيس التعليمي. مقاييس الاداء المستخدمة هي) الدقة والانضباط والاسترجاع ودرجة ال (F1أظهرت النتائج أن النهج المقترح أنتج نموذجًا عالي الجودة ، بدقة إجمالية تبلغ 8.959٪ ، واستدعاء كلي بنسبة97 ٪ لمجموعة البيانات المجمعة.
(COVID-19,NonCOVID)مجموعة البيانات المستخدمة هي 960 شريحة من الأشعة المقطعية تم جمعها من المرضى العراقيين / مستشفى ابن النفيس التعليمي. تم استخدام مقاييس الأداء في هذه الدراسة) الدقة والانضباط والاسترجاع ودرجة ال (F1أظهرت النتائج أن النهج المقترح أنتج نموذجًا عالي الجودة ، بدقة إجمالية تبلغ 96.9٪ ، واستدعاء كلي بنسبة 96.2٪ لمجموعة البيانات المجمعة.
Received 9/5/2022, Accepted 7/9/2022, Published Online First 20/1/2023
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