OCU-Net: A Novel U-Net Architecture for Enhanced Oral Cancer Segmentation

• URL: http://arxiv.org/abs/2310.02486v1
• Date: Tue, 3 Oct 2023 23:25:19 GMT
• Title: OCU-Net: A Novel U-Net Architecture for Enhanced Oral Cancer Segmentation
• Authors: Ahmed Albishri, Syed Jawad Hussain Shah, Yugyung Lee, Rong Wang
• Abstract summary: This study proposes OCU-Net, a pioneering U-Net image segmentation architecture exclusively designed to detect oral cancer. OCU-Net incorporates advanced deep learning modules, such as the Channel and Spatial Attention Fusion (CSAF) module. The incorporation of these modules showed superior performance for oral cancer segmentation for two datasets used in this research.
• Score: 22.652902408898733
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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