Böbrek Tümör Segmentasyonu İçin Unet ve Unet-ResNet Modellerinin Karşilaştirilmasi

Abstract

Kidney cancer is one of the types of cancer that can be difficult to diagnose and can be very complicated for physicians to diagnose. Especially in recent years, many new treatment methods for kidney cancer have been developed and some of them are still under development by scientists. These studies enable new treatment modalities for kidney cancer patients. In addition, renal tumors are one of the most insidious progressive tumor types. Many times it can be mistaken for other diseases. Especially until the last stage, patients may not even have a serious complaint. Therefore, conducting such studies is very important for early diagnosis. In this study, it is tried to segmentation with deep learning methods in order to help people who are dealing with difficulties of kidney cancer diagnosis. For this reason, Unet and Unet-ResNet models were compared. The Unet-ResNet model achieved 90.2% success for renal tumor segmentation, while the Unet model achieved 44.3% success for renal tumor segmentation. These results shed light on how successful and necessary the Unet-ResNet model can be in particular in studies on image segmentation. © 2019 IEEE.