دوره 22، شماره 2 - ( مجله مهندسی برق و الکترونیک ایران - جلد 22 شماره 2 1404 )
جلد 22 شماره 2 صفحات 141-129 |
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Mavaddati S, Azhari Lamraski H S. Brain Tumor Detection and Classification Based on MRI Images Using Deep Learning and Transfer Learning Models. Journal of Iranian Association of Electrical and Electronics Engineers 2025; 22 (2) :129-141
URL: http://jiaeee.com/article-1-1782-fa.html
URL: http://jiaeee.com/article-1-1782-fa.html
مودّتی سمیرا، اظهری لمراسکی هانیه سادات. تشخیص و طبقه‌بندی تومورهای مغزی با استفاده از مدل‌های یادگیری عمیق و یادگیری انتقالی بر پایه تصاویر MRI. نشریه مهندسی برق و الکترونیک ایران. 1404; 22 (2) :129-141
گروه مهندسی برق- دانشکده مهندسی و فناوری- دانشگاه مازندران
چکیده: (1061 مشاهده)
تومورهای مغزی یکی از شایع‌ترین و کشنده‌ترین نوع سرطان هستند. تشخیص دقیق و به موقع این دسته از تومور‌ها برای مدیریت بیماری و پیش آگهی موفق بیمار، ضروری است. همچنین تشخیص دقیق نوع تومور مغزی نقشی حیاتی در تعیین مسیر درمان ایفا می‌نماید. با شناخت نوع تومور، پزشک می‌تواند مناسب‌ترین روش درمانی را انتخاب کند که می‌تواند شامل جراحی، پرتودرمانی، شیمی درمانی یا ترکیبی از این روش‌ها باشد. همچنین نوع تومور به پیش‌بینی پیشرفت بیماری و کیفیت زندگی پس از درمان بیمار کمک می‌نماید. در سال‌های اخیر، یادگیری عمیق به عنوان ابزاری قدرتمند برای وظایف مختلف پردازش تصویر، از جمله تشخیص تومور مغزی، بکار گرفته شده است. در این مقاله، مدل‌های مختلف یادگیری عمیق مانند CNN، RNN، VGG16،InceptionV3 و رزنت101 به منظور تشخیص نوع تومور مغزی از مجموعه داده Figshare مبتنی‌بر تصاویر ام‌آر‌آی تومورهای مغزی گلیوم، مننژیوم، هیپوفیز، و بدون تومور بررسی و در نهایت یک مدل عمیق مناسب مبتنی‌بر رزنت 101 در ترکیب با یادگیری انتقالی پیشنهاد می‌شود. یافته‌های مقاله براساس معیارهای مختلف و نیز تست آماری نشان می‌دهد که مدل‌های عمیق می‌توانند به طور موثر برای تشخیص تومور مغزی مورد استفاده قرار گیرند. در این میان مدل عمیق رزنت101 توانسته است دقت 37/%98 در تشخیص چهار کلاس معرفی شده را بدست آورد. این مطالعه نشان می‌دهد که یادگیری عمیق پتانسیل قابل توجهی برای بهبود دقت تشخیص تومور مغزی را دارد.
واژه‌های کلیدی: تشخیص تومور مغزی، یادگیری عمیق، یادگیری انتقالی، مجموعه داده Figshare، رزنت 101، پردازش تصویر، شبکه‌های عصبی عمیق.
نوع مقاله: پژوهشي |
موضوع مقاله:
الکترونیک
دریافت: 1403/10/5 | پذیرش: 1403/12/25 | انتشار: 1404/5/24
دریافت: 1403/10/5 | پذیرش: 1403/12/25 | انتشار: 1404/5/24
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