مقارنة خوارزميات التعلم الآلي القائمة على تحليل المكونات الرئيسية لتصنيف كورونا باستخدام صور الصدر بالأشعة السينية
DOI:
https://doi.org/10.21123/bsj.2024.9422الكلمات المفتاحية:
صورالأشعة السينية للصدر، كوفيد-19 ، شجرة القرار، الانحدار التدرجي العشوائي، نظرية البايزي الساذج، التعلم الاليالملخص
أدى الانتشار السريع لجائحة كوفيد-19 إلى إجهاد أنظمة الرعاية الصحية العالمية، مما استلزم أساليب تشخيص فعالة. في حين أن تفاعل البوليميراز المتسلسل (PCR) واختبارات المستضدات شائعة، إلا أن لها حدودًا في السرعة والدقة. يعد تعزيز دقة تقنيات التصوير، وخاصة )الأشعة السينية للصدر( و)التصوير المقطعي المحوسب(، أمرًا بالغ الأهمية للكشف عن تشوهات الرئة المرتبطة بكوفيد-19. يُفضل استخدام الأشعة السينية للصدر، لكونه فعال من حيث التكلفة ويمكن الوصول إليه، على الأشعة المقطعية، لكن التشخيص الدقيق غالبًا ما يتطلب دعمًا تكنولوجيًا. ولمعالجة هذه المشكلة، تتوفر مجموعة بيانات شاملة لصور الأشعة السينية للصدر مصنفة إلى خمس فئات على Kaggle . تتضمن معالجة مثل هذه البيانات خطوات مثل تحويل (التدرج الرمادي، وضبط كثافة الصورة، وتغيير الحجم، واستخراج الميزات باستخدام تحليل المكونات الرئيسية). تقنيات التعلم الآلي، بما في ذلك (شجرة القرار)، و(الغابات العشوائية)، و(الانحدار التدرجي العشوائي)، و(الانحدار اللوجستي)، و( نظرية البايزي الساذج)، و (خوارزمية الجيران الاقرب)، هي تقنيات المستخدمة لتصنيف الصور. يُظهر (شجرة القرار) أعلى دقة بنسبة 88⸓، متفوقًا على النماذج الأخرى مثل ( نظرية البايزي الساذج 77⸓ )، و (خوارزمية الجيران الاقرب 71⸓ (، (الانحدار التدرجي العشوائي 70⸓ )، (الانحدار اللوجستي 74⸓)، الغابات العشوائية 45⸓ شجرة القرار إنه يتفوق باستمرار عبر مقاييس التقييم مثل درجة الضبط، والحساسية، الدقة، والاستدعاء، بمتوسط مرجح بنسبة 88⸓ . ومع ذلك، يعتمد اختيار النموذج الأمثل لتعلم الآلة على عوامل مثل خصائص مجموعة البيانات وتفاصيل التنفيذ. وبالتالي، يعد النظر بعناية في هذه العوامل أمرًا بالغ الأهمية عند اختيار نموذج تعلم الآلة لتشخيص كوفيد-19 عبر تصنيف صور الأشعة السينية للصدر.
Received 12/09/2023
Revised 19/04/2024
Accepted 21/04/2024
Published Online First 20/08/2024
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الحقوق الفكرية (c) 2024 Hussein Ahmed Ali, Walid Hariri, Nadia Smaoui Zghal, Dalenda Ben Aissa

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