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Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis.
1Department of General Surgery of Ruijin Hospital, Shanghai Institute of Digestive Surgery and Shanghai Key Laboratory for Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Frontiers in Oncology
|March 26, 2021
Summary
Artificial intelligence, specifically convolutional neural networks (CNNs), is advancing medical image analysis. This review highlights CNNs for disease detection and segmentation in medical imaging, crucial for accurate diagnosis.
Area of Science:
- Digital medicine and artificial intelligence in healthcare.
- Advanced medical image analysis techniques.
Background:
- Increasing volume of daily medical images necessitates intelligent diagnostic tools.
- Convolutional Neural Networks (CNNs) show rapid progress in artificial intelligence.
Purpose of the Study:
- To review the progression of object detection and semantic segmentation in medical imaging.
- To discuss accurate disease localization and boundary definition using AI.
Main Methods:
- Review of existing literature on CNN applications in medical imaging.
- Focus on object detection and semantic segmentation algorithms.
Main Results:
- CNNs are vital for medical imaging classification, object detection, and semantic segmentation.
- Object detection and semantic segmentation in medical imaging are less explored than classification.
Conclusions:
- CNN-based object detection and semantic segmentation are crucial for improving diagnostic accuracy.
- Further research is needed to define disease location and boundaries precisely using AI.
