البعد الكسري وتحليل الإنتروبيا للصور الطبية لتصنيف الأمراض على أساس  KNN

المؤلفون

  • سيلفاكومار آر مدرسة العلوم المتقدمة، معهد فيلور للتكنولوجيا، فيلور، 632014، تاميل نادو، الهند. https://orcid.org/0000-0002-9233-3763
  • كريشنا ماهاباترا مدرسة العلوم المتقدمة، معهد فيلور للتكنولوجيا، فيلور، 632014، تاميل نادو، الهند. https://orcid.org/0009-0001-8421-9805

DOI:

https://doi.org/10.21123/bsj.2024.10835

الكلمات المفتاحية:

البعد الكسري، إنتروبيا شانون، تحليل الصور الطبية، التصوير بالرنين المغناطيسي، الأشعة المقطعية، التحليل الإحصائي، التعلم الآلي، أقرب الجيران.

الملخص

شهدت دراسة الكشف عن الأمراض المستندة إلى الصور الطبية زيادة ملحوظة في الاهتمام والنجاح باستخدام الأساليب الحسابية. يقترح هذا العمل إطارًا جديدًا للكشف عن الأمراض في مراحل مبكرة من الصور الطبية، باستخدام النمذجة الرياضية والتعلم الآلي. نقدم مقياسين كميين جديدين للكشف عن مرض كوفيد-19 وأمراض الأورام: عدم اليقين في الصورة بناءً على إنتروبيا شانون وتعقيد الصورة بناءً على البعد الكسري. أظهرت نتائجنا أنه في الصور الإيجابية لـCOVID-19 أظهرت توزيعًا منحرفًا للبكسل بسبب المناطق الضبابية مما أدى إلى انخفاض قيم الإنتروبيا للحالات المريضة مقارنة بالحالات السليمة. أما الكمية الثانية، وهي البعد الكسوري، فقد تم قياسها بطريقة العد المربع التي تحدد مدى تعقيد الصورة. تم تطبيق نتائج كلا التقنيتين على تصنيف الصور باستخدام نموذج التعلم الآلي (ML) k-NN (أقرب جار). يوفر هذا الإطار الكامل نهجًا جديدًا وفريدًا لتحديد وتصنيف أنواع مختلفة من الصور بدقة تصنيف تبلغ ≈ 90% لفيروس Covid و≈ 70% للورم. يُظهر عملنا أن الأنتروبيا والأبعاد الكسرية يمكن أن تميز بين كوفيد-19 والمرضى الأصحاء، مما يجعلها واعدة بالتشخيص المبكر. تقدم هذه المخطوطة منهجية جديدة وفعالة حسابياً وقابلة للتفسير لتصنيف الأمراض والتي توفر الكشف عن المرض في مرحلة مبكرة.

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1.
البعد الكسري وتحليل الإنتروبيا للصور الطبية لتصنيف الأمراض على أساس  KNN. Baghdad Sci.J [انترنت]. [وثق 7 نوفمبر، 2024];22(5). موجود في: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/10835