التشخيص المبكر لمرض الزهايمر عن طريق استخراج مميزات الصور بالرسم البياني واكتشاف حافة كاني باستخدام الشبكة العصبية التلافيفية
محتوى المقالة الرئيسي
الملخص
يؤثر مرض الزهايمر (AD) بشكل متزايد على كبار السن ويعتبر أحد الامراض الرئيسية التي تؤدي الى الموت لأولئك الذين يبلغون من العمر 65 عامًا وأكثر. يتم استخدام طرق مختلفة للتعلم العميق للتشخيص التلقائي لهذا المرض، إلا أنها احيانا قد تحتوي على بعض القيود. يعد التعلم العميق من الطرق الحديثة التي تم استخدامها لكشف وتصنيف الصورة الطبية بسبب قدرته على استخراج ميزات الصور بشكل تلقائي. ومع ذلك، لا تزال هناك بعض القيود على استخدام التعلم العميق لتصنيف الصور الطبية بدقة عالية لأنه في بعض الأحيان لا يمكن استخراج الحواف الدقيقة، وقد يكون هناك بعض التشويه في الصور. يهدف هذا البحث إلى تطوير نظام تشخيص الدماغ بمساعدة الحاسوب (CABD) الذي يمكنه معرفة ما إذا كان فحص الدماغ يظهر مؤشرات على مرض الزهايمر ام لا. يستخدم النظام المقترح التصوير بالرنين المغناطيسي وطرق استخراج الميزات لتصنيف الصور الطبية. تستهدف هذه الورقة مجموعة بيانات مبادرة التصوير العصبي لمرض الزهايمر (ADNI) التي تتضمن التصوير بالرنين المغناطيسي الوظيفي والتصوير المقطعي بالإصدار البوزيتروني لتحديد مريض الزهايمر، والتي تم إنتاجها للأشخاص المصابين بمرض الزهايمر بالإضافة إلى الاشخاص غير المصابين. تستخدم التقنية المقترحة فحوصات الدماغ بالرنين المغناطيسي لاكتشاف وتصنيف السمات باستخدام تقنية استخراج ميزات الرسم البياني (HFE) ليتم دمجها مع اكتشاف حواف Canny الذي يمثل الصورة المدخلة ليتم ادخالها في الشبكة العصبية التلافيفية لغرض تصنيفها. تقوم هذه الإستراتيجية بتتبع حالات الاتجاه المتدرج في الصورة. حيث قدمت هذه التقنية المقترحة نتيجة تجريبية بلغت دقتها 97.7% لتصنيف صور الرنين المغناطيسي لمرض الزهايمر.
Received 29/09/2023
Revised 10/02/2024
Accepted 12/02/2024
Published 25/02/2024
تفاصيل المقالة
هذا العمل مرخص بموجب Creative Commons Attribution 4.0 International License.
كيفية الاقتباس
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