عرض تفاصيل البحث

ادناه جميع التفاصيل الخاصة بالبحث المطلوب عرضه. توفر هذه المنصة معلومات اساسية حول البحث, لمزيد من التفاصيل يرجى متابعة التصفح من خلال الضغط على الرابط الاصلي للبحث او رابط DOI.

عنوان البحث
Deepneuralidealnetworksforbraintumourimagesegmentation
عنوان المجلة
Proceedings Of Data Analytics And Management
ISSN-2770-4491
تفاصيل النشر
سنة النشر - 2023 / الفهرس الاصلي للمجلة - 572 : 3 (عدد الصفحات 12)
تصنيف البحث
علوم حاسبات - المجموعة العلمية
رابط DOI
#
رابط البحث
فتح في موقع المجلة الرسمي
البحث والاستدامة
الهدف 4 – التعليم الجيد   المزيد حول هذا الهدف
البحث والمجتمع
نعم , يدعم
الكلمات المفتاحية
Deep Learning, Residual Network, Medical images

اسم الباحثجهة الانتساببلد الباحث
صادق ثامر هلامه جامعة سومر العراق

The automated segmentation of brain tumours utilizing multimodal magnetic resonance imaging (MRI) is crucial in researching and monitoring disease progression. To aid in distinguishing gliomas into intertumoural classes, efficient and precise segmentation methods are utilized to differentiate gliomas into intratumourally categorized types. Deep learning algorithms outperform classical context-based computer vision techniques in circumstances that need the segmentation of objects into categories. Convolutional neural networks (CNNs) are extensively used in medical image segmentation, and they have significantly improved the accuracy of brain tumour segmentation in the present generation. Specifically, this research introduces a residual network (ResNet), a blend of two segmentation networks that employ a primary but simple combinative method to provide better and more accurate predictions. After each model was trained on the BraTS-20 challenge data, it was analysed to yield segmentation results. Among the different methodologies examined, ResNet produced the most accurate results compared to U-Net and was thus chosen and organized in many ways to arrive at the final forecast on the validation set. The ensemble acquired dice scores of 0.80 and 0.85 for the augmentation of the tumour, total cancer, and tumour core, respectively, demonstrating more excellent performance than the present technology in use.
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