اكتشاف COVID-19  من الأشعة السينية للصدر باستخدام إطار عمل التعلم العميق عبر تطبيق الهاتف المحمول
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الكلمات المفتاحية

Deep learning, CNN, MobileNet-V2, image processing, COVID-19, lung opacity, mobile display

كيفية الاقتباس

اكتشاف COVID-19  من الأشعة السينية للصدر باستخدام إطار عمل التعلم العميق عبر تطبيق الهاتف المحمول. (2025). مجلة الخوارزمي الهندسية, 21(1), 13-27. https://doi.org/10.22153/kej.2025.12.001

الملخص

تواجه األنظمة الصحية العالمية تحديات كبيرة نتيجة جائحة كوفيد،19- مما يؤكد على الحاجة لوسائل سريعة ودقيقة لتشخيص وكشف الفيروس. ظهرت الصور الشعاعية للصدر كأداة قيمة لتحديد التشوهات الرئوية التي تسببها كوفيد.19- يقترح هذا المشروع إطار عمل للتعلم العميق يستخدم صور األشعة السينية للكشف عن كوفيد،19- والذي يتم دمجه في تطبيق للهواتف المحمولة لتحقيق إمكانية الوصول الشاملة وسهولة االستخدام. يعتمد إطار العمل للتعلم العميق على تصميم شبكة عصبية تحتوي على طبقات تصفية تؤهلها لتعلم واستخالص سمات ذات صلة من صور األشعة السينية للصدر بشكل مستقل. يتم تدريب نموذج الشبكة العصبية باستخدام صور مصنفة لتعزيز قدرته على التمييز بين رئتين مصابتين بكوفيد19- وغير مصابتين. لخلق بيئة سهلة االستخدام، يتم إنشاء واجهة محمولة تتضمن نموذج عميق مدرب للتعلم العميق. يمكن للمستخدمين باستخدام كاميرا هواتفهم المحمولة تحميل صورة للصدر باألشعة السينية بكل سهولة وتقديمها عبرتطبيق الهاتف للتحليل. يقوم التطبيق المحمول بارسال الصورة للتحليل الى برنامح باستخدام نموذج تعلم عميق مدرب مسبقً الماتالب و ا، الذي يعيد درجة احتمالية تشير إلى احتمالية اإلصابة بكوفيد.19- تظهر النتائج فعالية طريقتنا من خالل تحقيق دقة عالية في اكتشاف كوفيد 1

 

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