Göğüs Kanseri Kitle Tespitinde Derin Öğrenme Modellerinin Karşılaştırılması: YOLOv8 ve U-Net

Abstract

Hastalıkların erken tespiti, özellikle kanser gibi hayati risk taşıyan durumlarda, tedavi sürecinin başarısı için kritik öneme sahiptir. Göğüs kanseri gibi hastalıklarda erken dönemde yapılan kitle tespiti, tedavi sürecinin etkinliği açısından belirleyici olabilir. Bu çalışma, göğüs görüntülerindeki kitle tespiti amacıyla YOLOv8 ve U-Net modellerinin performanslarını karşılaştırmıştır. İlk aşamada, her iki model de CBIS-DDSM ve INbreast veri setleri üzerinde değerlendirilmiştir. Elde edilen sonuçlar, YOLOv8 modelinin precision metriklerinde U-Net'e kıyasla daha yüksek performans gösterdiğini ortaya koymuştur. CBIS-DDSM veri setinde YOLOv8 0.800123 precision değeri elde ederken, U-Net 0.762345 değerine ulaşmıştır. INbreast veri setinde ise YOLOv8 0.785234 precision değerine sahipken, U-Net 0.742345 değerini elde etmiştir. Bu bulgular, YOLOv8'in özellikle nesne tespiti görevlerinde daha başarılı ve hızlı sonuçlar verdiğini, tıbbi görüntüleme gibi hızlı kararlar alınması gereken alanlarda daha verimli olduğunu göstermektedir. Gelecekteki çalışmalar, her iki modelin güçlü yönlerini birleştirerek hibrit çözümler geliştirebilir ve model hızlarını optimize ederek tıbbi teşhislerde daha hızlı ve doğru sonuçlar elde edilmesini sağlayabilir.

Keywords

• Göğüs Kanseri
• Kitle Tespiti
• Derin Öğrenme
• YOLOv8
• U-Net.

Comparison of Deep Learning Models for Breast Cancer Mass Detection: YOLOv8 and U-Net

Abstract

Early detection of diseases is critical to the success of the treatment process, especially in life-threatening conditions such as cancer. In diseases such as breast cancer, early mass detection can be decisive for the effectiveness of the treatment process. This study compares the performance of YOLOv8 and U-Net models for mass detection in breast images. In the first stage, both models are evaluated on CBIS-DDSM and INbreast datasets. The results show that the YOLOv8 model outperforms U-Net in precision metrics. In the CBIS-DDSM dataset, YOLOv8 achieved a precision value of 0.800123, while U-Net achieved 0.762345. In the INbreast dataset, YOLOv8 achieved a precision value of 0.785234, while U-Net achieved a value of 0.742345. These findings show that YOLOv8 provides more successful and faster results, especially in object detection tasks, and is more efficient in areas where fast decisions need to be made, such as medical imaging. Future studies can develop hybrid solutions by combining the strengths of both models and optimize model speeds to achieve faster and more accurate results in medical diagnostics.

Keywords

• Breast Cancer
• Mass Detection
• Deep Learning
• YOLOv8
• U-Net.

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