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Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using
Jesus A Basurto-Hurtado1,2, Irving A Cruz-Albarran1,2, Manuel Toledano-Ayala3
1C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico.
Cancers
|July 27, 2022
Summary
This review covers image generation and processing for early breast cancer detection. Future methods should integrate artificial intelligence for improved accuracy and reliability in diagnosing malignant lesions.
Area of Science:
- Medical Imaging
- Oncology
- Artificial Intelligence
Background:
- Breast cancer is a leading cause of death in women globally, necessitating early diagnosis for improved survival rates.
- Current research often focuses on specific aspects of breast cancer detection, lacking a holistic view from image generation to interpretation.
Purpose of the Study:
- To provide a comprehensive state-of-the-art review of image generation and processing techniques for breast cancer detection.
- To discuss potential candidates for image generation and processing in breast cancer diagnostics.
Main Methods:
- Systematic literature review of image generation techniques.
- Analysis of image processing methodologies for breast cancer detection.
- Discussion of artificial intelligence integration in diagnostic workflows.
Main Results:
- Identified key image generation and processing techniques relevant to breast cancer detection.
- Highlighted the need for integrated approaches combining AI and categorical data.
- Discussed the potential for novel methodologies to enhance accuracy and reliability.
Conclusions:
- A comprehensive understanding of image generation and processing is crucial for advancing breast cancer detection.
- Future research should focus on integrating artificial intelligence with existing data for more precise and reliable diagnostic tools.
- Mitigating misclassifications requires novel methodologies that ensure accuracy, precision, and reliability in breast cancer imaging analysis.
Keywords:
artificial intelligencebreast cancerimage processingmagnetic resonancemammographythermographyultrasound
