Modified Double U-Net Architecture for Medical Image Segmentation

Semantic segmentation is the process of labeling each pixel of an input image with its appropriate class. In biomedical image processing, semantic segmentation is a critical preprocessing step. After the evolution of deep learning, specifically, the U-Net, which is based on the encoder–decoder model, researchers have extensively used several modified versions of U-Net for semantic segmentation. This manuscript presents a novel architecture called modified Double U-Net for various biomedical image segmentation tasks. The modified Double U-Net takes full advantage of Double U-Net and ensemble learning. It is the combination of two U-Net architectures stacked on top of each other. The first U-Net uses an ensemble of pretrained Xception, DenseNet, and VGG-19. We have deliberately used a pretrained model so that the features learned from the ImageNet classification task can be reused for our semantic segmentation task. Another U-Net is stacked at the bottom to capture more information that can be used in our semantic segmentation task. The performance of our proposed model has been evaluated on five different medical datasets, namely, the Data Science Bowl Challenge-2018, the CVC-ClinicDB dataset, which has complex images, such as smaller and flat polyps, the ISIC-2018 challenge dataset, the Kvasir-Instrument dataset, and the INBreast dataset achieving state-of-the-art performance. Conducting fivefold cross-validation on the five datasets mentioned above intersection over union of
$89.13~\pm ~2.88$
,
$85.22~\pm ~1.26$
,
$83.13~\pm ~3.56$
,
$85.22~\pm ~1.26$
, and
$95.22~\pm ~2.56$
were obtained.