OCU-Net: A Novel U-Net Architecture for Enhanced Oral Cancer
  Segmentation
Ahmed Albishri Syed Jawad Hussain Shah Yugyung Lee Rong Wang
Accurate detection of oral cancer is crucial for improving patient outcomes.
However, the field faces two key challenges: the scarcity of deep
learning-based image segmentation research specifically targeting oral cancer
and the lack of annotated data. Our study proposes OCU-Net, a pioneering U-Net
image segmentation architecture exclusively designed to detect oral cancer in
hematoxylin and eosin (H&E) stained image datasets. OCU-Net incorporates
advanced deep learning modules, such as the Channel and Spatial Attention
Fusion (CSAF) module, a novel and innovative feature that emphasizes important
channel and spatial areas in H&E images while exploring contextual information.
In addition, OCU-Net integrates other innovative components such as
Squeeze-and-Excite (SE) attention module, Atrous Spatial Pyramid Pooling (ASPP)
module, residual blocks, and multi-scale fusion. The incorporation of these
modules showed superior performance for oral cancer segmentation for two
datasets used in this research. Furthermore, we effectively utilized the
efficient ImageNet pre-trained MobileNet-V2 model as a backbone of our OCU-Net
to create OCU-Netm, an enhanced version achieving state-of-the-art results.
Comprehensive evaluation demonstrates that OCU-Net and OCU-Netm outperformed
existing segmentation methods, highlighting their precision in identifying
cancer cells in H&E images from OCDC and ORCA datasets.