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Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological
R Rashmi1, Keerthana Prasad2, Chethana Babu K Udupa3
1Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, India.
Journal of Medical Systems
|December 3, 2021
Summary
This review explores traditional and deep learning methods for analyzing breast cancer histopathology images. It highlights the need for computer-aided diagnostic systems to improve accuracy and efficiency in breast cancer detection.
Area of Science:
- Oncology
- Medical Imaging
- Computer Science
Background:
- Breast cancer is a leading global health concern, necessitating accurate diagnostic tools.
- Current diagnostic methods rely on imaging and manual histopathological analysis, which can be time-consuming and subjective.
- Computer-aided diagnostic (CAD) systems offer a potential solution to enhance the accuracy and efficiency of breast cancer diagnosis.
Purpose of the Study:
- To review traditional and deep learning-based methods for analyzing breast cancer histopathology images (BCHI).
- To discuss the characteristics of BCHI and identify key regions of interest for CAD system development.
- To summarize current trends in medical image processing techniques and outline future research directions.
Main Methods:
- Comprehensive literature review of traditional and deep learning approaches for BCHI analysis.
- Discussion of image processing techniques relevant to histopathological slide analysis.
- Analysis of challenges and future scope in the field.
Main Results:
- Various traditional and deep learning methods exist for BCHI analysis.
- Identifying regions of interest is critical for developing effective CAD systems.
- Recent advancements in computational power facilitate the application of advanced image processing techniques.
Conclusions:
- Computer-aided diagnostic systems are essential for improving breast cancer diagnosis accuracy and consistency.
- Further research into deep learning and advanced image processing holds significant promise for the future of BCHI analysis.
- Addressing the challenges in BCHI analysis is crucial for advancing early breast cancer detection and patient outcomes.
