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Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer
Summary
Neural networks (NNs) can significantly improve breast cancer detection accuracy and speed by automating image analysis. This review guides the development of NN algorithms for enhanced medical imaging specificity and sensitivity.
Area of Science:
- Medical imaging
- Artificial intelligence in healthcare
- Oncology
Background:
- Manual analysis of medical images for breast cancer diagnosis is time-consuming.
- Automated classifiers, particularly neural networks (NNs), offer improved accuracy and efficiency.
- A need exists for comprehensive reviews on NN applications in medical imaging.
Purpose of the Study:
- To review recent literature on neural network techniques in medical imaging.
- To highlight the state-of-the-art NN applications for enhanced breast cancer detection.
- To explore NN versatility across various medical imaging fields.
Main Methods:
- Systematic review of recent scientific publications.
- Analysis of neural network types and data feeding methods used in medical imaging.
- Focus on neural network applications in breast cancer detection and hybrid adaptations.
Main Results:
- Neural networks are crucial for automated breast cancer detection, improving diagnostic accuracy and reducing analysis time.
- NNs are applicable beyond breast cancer, demonstrating broad utility in medical imaging.
- Various NN architectures and data inputs are employed, with hybrid models showing promise.
Conclusions:
- Neural networks are vital for advancing medical imaging analysis, especially in oncology.
- Further research into NN algorithms can enhance diagnostic specificity and sensitivity.
- The review provides guidance for developing more effective AI-driven diagnostic tools.