A sensitivity analysis for polyp segmentation with U-Net

Colorectal Cancer (CRC) is one of the most common cancer diseases in the world. Early diagnosis of the disease is of great importance for the recovery of the patient. Colonoscopy is the gold standard procedure used in the diagnosis of CRC. In this context, this study focused on the detection of polyps with high accuracy in order to contribute to the early diagnosis of CRC. Within the scope of the study, polyp segmentation was performed on the public CVC-Clinic DB polyp dataset. In the study, the basic U-Net model and its derivatives (modified U-Net, modified U-Net with transfer learning (VGG-16, VGG-19) in the encoding part) were used for the segmentation process. For sensitivity analysis, models were trained on three separate datasets prepared with different preprocessing methods in addition to the raw dataset with k-fold cross validations (k = 2,3,4) and different batch numbers (1,2,3,4,5) in each cross validation. As a result of the analysis, the best performance was obtained as 0.868, 0.799, 0.873 and 0.994 for Dice, Jaccard, Sensitivity, Specificity when the batch size was taken as 1 with fourfold cross validation in the modified U-Net trained with the Discrete Wavelet Transform (DWT) dataset. This model and its parameters were then tested with public datasets Kvasir-Seg and Etis-Larib Polyp DB. Moreover, different models were trained with the parameters of the most successful model. The results of all analyzes were interpreted and compared with the literature.