TY - GEN
T1 - Deep Oral Cancer Lesion Segmentation with Heterogeneous Features
AU - Huang, Shih Yang
AU - Chiou, Chien Yu
AU - Tan, Yi Siang
AU - Chen, Chih Yang
AU - Chung, Pau Choo
N1 - Funding Information:
This work was supported in part by the National Science and Technology Council, Taiwan under Grant MOST110-2634-F-006-022
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - About 650,000 new cases of oral cavity cancer occur every year in the world, and cause more than 330,000 deaths. If oral cancer is diagnosed at an early stage, the overall 5-year survival rate is over 70%, while it drops to less than 40% if detected at a late stage. Thus, early detection of oral cancer is important. Visual non-invasive examination is an efficient and feasible approach for performing a preliminary diagnosis of oral cancer. In this paper, we propose a fully convolutional network (FCN) based model to segment cancer and precancer lesion regions in the oral cavity. In addition to the RGB channels of the input image, we append features of Gabor filter and wavelet filter that show strong response at cancer and precancer regions. We also propose a refine stage before the decision layer of FCN to preventing weight dominating problem when reducing high dimension features to small number of classes. In the experiments on oral cancer dataset, the IOU, sensitivity, and specificity of the proposed network achieves 0.586, 0.883, 0.726 respectively. The experimental results show the effectiveness of our method.
AB - About 650,000 new cases of oral cavity cancer occur every year in the world, and cause more than 330,000 deaths. If oral cancer is diagnosed at an early stage, the overall 5-year survival rate is over 70%, while it drops to less than 40% if detected at a late stage. Thus, early detection of oral cancer is important. Visual non-invasive examination is an efficient and feasible approach for performing a preliminary diagnosis of oral cancer. In this paper, we propose a fully convolutional network (FCN) based model to segment cancer and precancer lesion regions in the oral cavity. In addition to the RGB channels of the input image, we append features of Gabor filter and wavelet filter that show strong response at cancer and precancer regions. We also propose a refine stage before the decision layer of FCN to preventing weight dominating problem when reducing high dimension features to small number of classes. In the experiments on oral cancer dataset, the IOU, sensitivity, and specificity of the proposed network achieves 0.586, 0.883, 0.726 respectively. The experimental results show the effectiveness of our method.
UR - http://www.scopus.com/inward/record.url?scp=85146274371&partnerID=8YFLogxK
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U2 - 10.1109/RASSE54974.2022.9989871
DO - 10.1109/RASSE54974.2022.9989871
M3 - Conference contribution
AN - SCOPUS:85146274371
T3 - RASSE 2022 - IEEE International Conference on Recent Advances in Systems Science and Engineering, Symposium Proceedings
BT - RASSE 2022 - IEEE International Conference on Recent Advances in Systems Science and Engineering, Symposium Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Recent Advances in Systems Science and Engineering, RASSE 2022
Y2 - 7 November 2022 through 10 November 2022
ER -