تشخیص تومور مغزی در تصاویر رزونانس مغناطیسی با استفاده از شبکه عصبی کانولوشنی عمیق
محورهای موضوعی : مهندسی الکترونیکمیترا افسری نژاد 1 , نبي اله شیری 2 , رامین براتی 3
1 - گروه مهندسی برق، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران
2 - گروه مهندسی برق، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران
3 - گروه مهندسی برق، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران
کلید واژه: تومور مغزی, شبکه عصبی کانولوشنی, تصویربرداری پزشکی, یادگیری عمیق, طبقه‌بندی تصویر, ,
چکیده مقاله :
در این مقاله، تشخیص تومور مغز از طریق به کارگیری تکنیک‌های پیشرفته یادگیری عمیق مورد بررسی قرار گرفته است. رویکرد این مطالعه شامل توسعه و آموزش یک معماری جامع از شبکه عصبی کانولوشنی (CNN) با بهره‌گیری از یک مجموعه داده گسترده از تصاویر رزونانس مغناطیسی مغز (MRI) می¬باشد، مدل پیشنهادی در طبقه‌بندی بافت معمولی مغز و مناطق تحت تأثیر تومور بسیار توانمند است. این معماری شامل لایه‌های متعدد از جمله لایه‌های کانولوشنی، نرمال‌سازی دسته‌ای و لایه‌های پولینگ است که در نهایت به یک لایه قوی طبقه‌بندی منجر می‌شود. از طریق آموزش دقیق و بهینه‌سازی، شبکه عصبی کانولوشنی معرفی‌شده توانسته است در طبقه‌بندی تومور مغز به دقت بالایی دست یابد. اثربخشی این مدل پیشنهادی از طریق آزمایشات جامع به نمایش گذاشته شده که نشان‌دهنده قابلیت آن در تشخیص دقیق تومور مغز است.
In this paper, brain tumor detection is addressed through the application of advanced deep-learning techniques. The approach involves the development and training of a comprehensive convolutional neural network (CNN) architecture. Leveraging an extensive dataset of brain magnetic resonance imaging (MRI), the proposed model expresses its proficiency in the classification of normal brain tissue and tumor-affected regions. The architecture encompasses multiple layers, including convolutional, batch normalization, and pooling layers, culminating in a robust classification layer. Through rigorous training and optimization, the introduced CNN achieves a high level of accuracy in brain tumor classification. The effectiveness of the proposed model is showcased through comprehensive experimentation, demonstrating its potential to significantly contribute to the medical field’s efforts in precise brain tumor diagnosis.
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