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[Research progress on colorectal cancer identification based on convolutional neural network].
Xingliang Pan1,2, Ke Tong1,2, Chengdong Yan1,2
1The School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, Sichuan 643000, P. R. China.
Summary
Convolutional neural networks (CNNs) show great promise for improving colorectal cancer (CRC) diagnosis. These advanced algorithms enhance the accuracy and efficiency of CRC classification and segmentation, potentially lowering healthcare costs.
Area of Science:
- Oncology
- Medical Imaging
- Artificial Intelligence
Background:
- Colorectal cancer (CRC) poses a significant global health threat.
- Accurate identification of CRC is challenging due to indistinct tumor boundaries.
- Convolutional Neural Networks (CNNs) are increasingly used in medical image analysis.
Purpose of the Study:
- To highlight the critical need for CNNs in clinical colorectal cancer diagnosis.
- To review current research on CNN applications in CRC classification and segmentation.
- To discuss methods for optimizing CNN performance in this domain.
Main Methods:
- Review of existing literature on CNNs for colorectal cancer.
- Analysis of CNN architectures and their modifications for CRC tasks.
- Exploration of performance optimization strategies for CNN models.
Main Results:
- CNNs demonstrate significant potential for automated CRC classification and segmentation.
- Various CNN models and optimization techniques have been developed for improved accuracy.
- Challenges and future research directions in CNN-based CRC analysis are identified.
Conclusions:
- CNNs are essential tools for advancing the clinical diagnosis of colorectal cancer.
- Further development and application of CNNs can enhance diagnostic efficiency and reduce costs.
- Continued research is needed to overcome current challenges and realize the full potential of CNNs in CRC detection.
