Detection of Esophageal Cancer Lesions Based on CBAM Faster R-CNN
DOI:
https://doi.org/10.53469/jtpes.2023.03(12).06Keywords:
Esophageal Cancer, Anti-Apoptotic Gene, Faster RCNN, Object DetectionAbstract
Esophageal cancer is a common malignant tumor in daily life, which seriously affects human health. Esophageal cancer in China. The incidence rate is among the highest in the world, and there are a large number of new cases of esophageal cancer every year. At present, the diagnosis of esophageal cancer is mainly based on the use of electronic gastroscopy, which reflects the observed situation on the fluorescent screen and conducts detection through the fluorescent screen. With the increasing number of patients, the pressure and intensity of the doctor's work are getting greater and greater[1]. From the perspective of molecular level, the occurrence and development of esophageal cancer, like other cancers, are related to the activation of proto-oncogenes and the inhibition of anti-apoptosis genes. CBAM Faster R-CNN is proposed to solve the problems such as the esophageal region is not obvious and the background region occupies a large proportion in the feature map obtained by the backbone network of Faster R-CNN in barium meal imaging. CBAM is added to the convolutional attention module CBAM on the basis of the original Faster R-CNN model. To enhance the saliency of the features of the esophageal region in the feature map. CBAM Faster R-CNN model was used to train the training set after data enhancement, and Recall, Precision and AP values were used for evaluation and analysis.
References
CIRSHICK R,DONAHUE J.DARRELL T et al. Richfeature hierachies for accurate object detection andsemantic segmentation [C] //IEEE. Computer Visionand Pattern Recognition. Columbus: IEEE,2014: 580587.
LAZEBNIK S,SCHMID C,PONCE J. Beyond bags ofSpatial pyramid matching for recognizingfeatures'natural scene categories [C] //IEEE. Computer SocietyConferenceComputerVision andPatternonRecognition. New York: IEEE,2006: 2169-2178.
CIRSHICK R. Fast R-CNN [C] //IEEE. InternationalSantiago: IEEE Conference on Computer Vision.2015: 1440-1448.
Chang Che, Bo Liu, Shulin Li, Jiaxin Huang, and Hao Hu. Deep learning for precise robot position prediction in logistics. Journal of Theory and Practice of Engineering Science, 3(10):36–41, 2023.DOI: 10.1021/acs.jctc.3c00031.
Hao Hu, Shulin Li, Jiaxin Huang, Bo Liu, and Change Che. Casting product image data for quality inspection with xception and data augmentation. Journal of Theory and Practice of Engineering Science, 3(10):42–46, 2023. https://doi.org/10.53469/jtpes.2023.03(10).06.
REN S O,HE K M,CIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with regionproposal networks L] . IEEE Transactions on PatternAnalysis and Machine Intelligence , 2017, 39 ( 6 ) :1137-1149.
WOO S,PARK J,LEE J Y, et a. CBAM:Convolutional block attention module [C] //Springer.European Conference on Computer Vision. Munish:Springer,2018: 3-19.
Chang Che, Qunwei Lin, Xinyu Zhao, Jiaxin Huang, and Liqiang Yu. 2023. Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation. In Proceedings of the 2023 6th International Conference on Big Data Technologies (ICBDT '23). Association for Computing Machinery, New York, NY, USA, 414–418. https://doi.org/10.1145/3627377.3627442.
Lin, Q., Che, C., Hu, H., Zhao, X., & Li, S. (2023). A Comprehensive Study on Early Alzheimer’s Disease Detection through Advanced Machine Learning Techniques on MRI Data. Academic Journal of Science and Technology, 8(1), 281–285.DOI: 10.1111/jgs.18617.
Che, C., Hu, H., Zhao, X., Li, S., & Lin, Q. (2023). Advancing Cancer Document Classification with R andom Forest. Academic Journal of Science and Technology, 8(1), 278–280. https://doi.org/10.54097/ajst.v8i1.14333.
Tianbo, Song, Hu Weijun, Cai Jiangfeng, Liu Weijia, Yuan Quan, and He Kun. "Bio-inspired Swarm Intelligence: a Flocking Project With Group Object Recognition." In 2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE), pp. 834-837. IEEE, 2023.DOI: 10.1109/mce.2022.3206678.
VerySIMONYAN K,ZISSERMANA .deepconvolutional networks for large scale image recognitionC] //IEEE. Computer Vision and Pattern Recognition.Columbus: IEEE,2014: 1-15
LITJENS C,KOOI T,BEJNORDI B E,et al. A surveyon deep learning in medical image analysis l] . Medicallmage Analysis, 2017, 42: 60 88.
Yao, J., Zou, Y., Du, S., Wu, H., & Yuan, B. Progress in the Application of Artificial Intelligence in Ultrasound Diagnosis of Breast Cancer. DOI:https://api.semanticscholar.org/Corpus.
XIE H,FANG S,ZHA Z J, et al. Convolutiona.ACMattention networks for scene text recognitionTransactions on Multimedia ComputingCommunicationsand Applications , 2019 , 15( 1s) : 1-17.
Li L, Xu C, Wu W, et al. Zero-resource knowledge-grounded dialogue generation[J]. Advances in Neural Information Processing Systems, 2020, 33: 8475-8485. DOI: https://doi.org/10.48550/arXiv.2008.12918.
SOMMEN FV D,ZINGER S,SCHOON E J, et al.Supportive automaticesophagealannotation of earlycancer using localgaborand color[J]featuresNeurocomputing ,2014,144( 20) : 92-106.
SCHOON E J,SOMMEN F V D,ZINCER S, et al.Computer aided delineation of early neoplasia in barrettsesophagus using high definition endoscopic images llGastrointestinal E.
Zhang, Q., Cai, G., Cai, M., Qian, J., & Song, T. (2023). Deep Learning Model Aids Breast Cancer Detection. Frontiers in Computing and Intelligent Systems, 6(1), 99-102.
Xu, J., Pan, L., Zeng, Q., Sun, W., & Wan, W. (2023). Based on TPUGRAPHS Predicting Model Runtimes Using Graph Neural Networks. Frontiers in Computing and Intelligent Systems, 6(1), 66-69.
Liu, B., Yu, L., Che, C., Lin, Q., Hu, H., & Zhao, X. (2023). Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms. arXiv preprint arXiv:2312.12872.
Miao, T., Zepeng, S., Xingnan, W., Kuo, W., & Yuxiang, L. (2023). The Application of Artificial Intelligence in Medical Diagnostics: A New Frontier. Academic Journal of Science and Technology, 8(2), 57-61.
Hong, Z., Yan, L., Jize, X., Yixu, W., Yuxiang, L. (2023). Improvement of Deep Learning Model for Gastrointestinal Tract Segmentation Surgery. Frontiers in Computing and Intelligent Systems, 6(1), 103-106.
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Copyright (c) 2023 Bo Liu, Xinyu Zhao, Hao Hu, Qunwei Lin, Jiaxin Huang
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