Ahmet Furkan BAYRAM, Caglar GURKAN, Abdulkadir BUDAK, Hakan KARATAŞ

9318

Fully Automatic End-to-End Convolutional Neural Networks-Based Pancreatic Tumor Segmentation on CT Modality

Fully Automatic End-to-End Convolutional Neural Networks-Based Pancreatic Tumor Segmentation on CT Modality

The pancreas is one of the vital organs in the human body. Early diagnosis of a disease in the pancreas is critical. In this way, the effects of pancreas diseases, especially pancreatic cancer on the person are decreased. With this purpose, artificial intelligence-assisted pancreatic cancer segmentation was performed for early diagnosis in this paper. For this aim, several state-of-the-art segmentation networks, UNet, LinkNet, SegNet, SQ-Net, DABNet, EDANet, and ESNet were used in this study. In the comparative analysis, the best segmentation performance has been achieved by SQ-Net. SQ-Net has achieved a 0.917 dice score, 0.847 IoU score, 0.920 sensitivity, 1.000 specificity, 0.914 precision, and 0.999 accuracy. Considering these results, an artificial intelligence-based decision support system was created in the study.
Keywords:

Pancreas Pancreatic Tumor, Deep Learning, Convolutional Neural Networks, Segmentation,

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Turkish Journal of Forecasting-Cover
  • Yayın Aralığı: Yılda 2 Sayı
  • Başlangıç: 2017
  • Yayıncı: Giresun Üniversitesi
Arşiv
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