Proposed Mixed Modified Inception and Self Attention U-Net for Medical Image Segmentation: MMIAU-Net

Abstract

Deep learning is an artificial intelligence method that achieves successful results in many areas, especially medical image segmentation. It is crucial to reach definitive conclusions by performing precise analysis of medical images, which are vital for human health. Thanks to their maximum computational complexity, deep learning methods can capture even the smallest disease detail that may be overlooked. U-Net deep learning model is the most popular architecture due to its high success in this field. However, since the accuracy values in segmentation give different results in each dataset, there is always a need to improve its performance. In order to make a comprehensive comparison, three independent medical datasets were used in this study: PH2, BOWL 2018, CVC-ClinicDB, which are publicly accessible, and U-Net, U-Net++, Attention U-Net, Residual U-Net, Residual Attention U-Net, TransUNet and Swin-Unet, and also with Self Attention U-Net and MMIAU-Net, which were modified for this study. After the analysis, it was seen that the proposed model MMIAU-Net reached higher performances by using fewer parameters.

Keywords

• Deep learning
• U-Net
• Segmentation
• Medical image processing
• Computer vision

Tıbbi Görüntü Bölütleme için MMIAU-Net Mimarisi Önerisi

Abstract

Derin öğrenme; başta tıbbi görüntü segmentasyonu olmak üzere birçok alanda başarılı sonuçlar elde eden bir yapay zeka yöntemidir. İnsan sağlığı için hayati önem taşıyan medikal görüntülerde, hassas analiz yapılarak kesin sonuçlara varılması mühimdir. Derin öğrenme yöntemleri yüksek hesaplama karmaşıklığı sayesinde gözden kaçabilecek en küçük hastalık detayını bile yakalayabilmektedir. U-Net derin öğrenme modeli bu alandaki yüksek başarısından dolayı en popüler mimaridir. Ancak segmentasyondaki doğruluk değerleri her veri kümesinde farklı sonuçlar verdiğinden performansının iyileştirilmesine her daim ihtiyaç vardır. Kapsamlı karşılaştırma yapabilmek için bu çalışmada, herkesin erişimine açık olan PH2, BOWL 2018, CVC-ClinicDB olmak üzere üç bağımsız tıbbi veri kümesi kullanılmış ve U-Net, U-Net++, Attention U-Net, Residual U-Net, Residual Attention U-Net, TransUNet ile Swin-Unet ve son olarak da bu çalışma için modifiye edilen Self Attention U-Net ve MMIAU-Net ile eğitimler yapılmıştır. Analizler sonucunda önerilen MMIAU-Net modelin daha az parametre kullanarak daha yüksek performanslara ulaştığı görülmüştür.

Keywords

• Derin öğrenme
• U-Net
• Segmentasyon
• Tıbbi görüntü işleme
• Bilgisayar görü

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