Deep Learning-based Bio-Medical Image Segmentation using UNet Architecture and Transfer Learning
This work addresses biomedical image segmentation for researchers, but it is incremental as it applies existing methods to standard datasets.
The paper tackled biomedical image segmentation by implementing a UNet architecture from scratch and applying transfer learning with a modified UNet, showing that the transfer learning model achieved better performance than the scratch implementation.
Image segmentation is a branch of computer vision that is widely used in real world applications including biomedical image processing. With recent advancement of deep learning, image segmentation has achieved at a very high level performance. Recently, UNet architecture is found as the core of novel deep learning segmentation methods. In this paper we implement UNet architecture from scratch with using basic blocks in Pytorch and evaluate its performance on multiple biomedical image datasets. We also use transfer learning to apply novel modified UNet segmentation packages on the biomedical image datasets. We fine tune the pre-trained transferred model with each specific dataset. We compare its performance with our fundamental UNet implementation. We show that transferred learning model has better performance in image segmentation than UNet model that is implemented from scratch.