***************«بسم الله الرحمن الرحیم» نشریه علمی «پردازش علائم و داده‌ها» با مجوز رسمی از کمیسیون نشریات وزارت علوم، تحقیقات و فناوری، صاحب امتیاز: پژوهشگاه توسعه فناوری‌های پیشرفته ***************
Signal and Data Processing Journal A scientific journal officially licensed by the Commission for Scientific Publications of the (MSRT). Publisher: Research Ceter for Developmen of Technologies
دوره 23، شماره 1 - ( 3-1405 )
جلد 23 شماره 1 صفحات 82-61 |
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Mohammadi S, Taghavian Asiabi Z, Babagoli M. Multi-class Lung Disease Detection from Enhanced CXR Images Using Ensemble Learning. JSDP 2026; 23 (1) : 5
URL: http://jsdp.rcisp.ac.ir/article-1-1475-fa.html
URL: http://jsdp.rcisp.ac.ir/article-1-1475-fa.html
محمدی شهریار، تقویان آسیابی زهرا، باباگلی مهدی. تشخیص چندکلاسۀ بیماری‌های ریوی از روی تصاویر بهبود‌یافتۀ رادیوگرافی قفسۀ سینه مبتنی‌بر یادگیری تجمیعی. پردازش علائم و داده‌ها. 1405; 23 (1) :61-82
دانشیار، دانشکدۀ مهندسی صنایع، دانشگاه خواجه نصیرالدین طوسی، تهران، ایران
چکیده: (546 مشاهده)
در این مقاله، یک روش ترکیبی طبقه‌بندی چندکلاسه مبتنی‌بر الگوریتم‌های پیشرفته یادگیری عمیق ارائه شده‌است. پایگاه داده COVID-19، که شامل مجموعه‌ای از تصاویر رادیوگرافی قفسه سینۀ[1] عمومی است، برای بررسی تأثیر روش‌‌های تصویربرداری ساده، بهبودیافته و بخش‌بندی‌شده مورد استفاده قرار گرفته است؛ علاوه‌براین، مزایای نهفته ترکیب تصاویر بخش‌بندی‌شده -بهبودیافته و بهبودیافته- بخش‌بندی‌شده نیز بررسی شده‌است؛ همچنین، به‌طور خاص تأثیر ترتیب اجرای مراحل بهبود و بخش‌بندی نیز ارزیابی شده‌است. بخش‌بندی ریه با استفاده از مدل U-Net انجام شده‌است؛ درحالی‌که بهبود تصاویر خام و بخش‌بندی‌شدۀ رادیوگرافی قفسه سینه با استفاده از یک شبکه عصبی پیشرفته صورت گرفته است. در مرحله پیش‌پردازش، تصاویر رادیوگرافی قفسه سینه تغییر اندازه داده شده و نرمال‌سازی می‌شوند؛ سپس به هفت مدل یادگیری انتقالی شامل VGG16، VGG19، ResNet50V2، InceptionV3، MobileNet، InceptionResNetV2 و DenseNet201 ورودی داده می‌شوند. قدرت این مدل‌ها از طریق یک رویکرد گروهی ترکیب شده و چهارچوب بهبودیافته پیشنهادی عملکرد برتری را با دقت 97،03 درصد نشان داده و از پژوهش‌های اخیر موجود پیشی گرفته است.
[1] Chest X-Ray (CXR)
شماره‌ی مقاله: 5
واژه‌های کلیدی: بیماری‌های ریوی، تصاویر رادیوگرافی قفسه سینه، یادگیری تجمیعی، یادگیری انتقالی، پردازش تصویر.
نوع مطالعه: پژوهشي |
موضوع مقاله:
مقالات گروه علائم حیاتی ( مرتبط با مهندسی پزشکی)
دریافت: 1404/4/2 | پذیرش: 1405/2/15 | انتشار: 1405/3/31 | انتشار الکترونیک: 1405/3/31
دریافت: 1404/4/2 | پذیرش: 1405/2/15 | انتشار: 1405/3/31 | انتشار الکترونیک: 1405/3/31
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