Effective Image Processing and Segmentation-Based Machine Learning Techniques for Diagnosis of Breast Cancer

Area of Science:

• Medical Imaging
• Machine Learning
• Oncology

Background:

• Breast cancer is a leading cause of death in women, with early detection crucial for successful treatment.
• Current prevention methods are limited, highlighting the need for improved diagnostic tools.
• Digital mammography is effective for early detection, but further enhancements can improve outcomes.

Purpose of the Study:

• To develop and evaluate an image processing and machine learning framework for enhanced breast cancer detection.
• To improve the accuracy and efficiency of early breast cancer identification from mammography images.
• To explore the potential of advanced image processing techniques in improving breast cancer survival rates.

Main Methods:

• A framework utilizing mammography images as input data.
• Contrast Limited Adaptive Histogram Equalization (CLAHE) for image quality enhancement and noise reduction.
• Image segmentation techniques to identify objects and delineate boundaries.
• Classification of preprocessed images using machine learning algorithms like fuzzy SVM, Bayesian classifier, and random forest.

Main Results:

• The framework successfully processes mammography images to improve their quality.
• Image segmentation aids in the identification of potential abnormalities.
• Machine learning classifiers demonstrate potential in categorizing enhanced images for detection.

Conclusions:

• The proposed framework offers a promising approach to enhance early breast cancer detection through image processing and machine learning.
• Improved image quality and segmentation can lead to more accurate diagnoses.
• Further research and validation of this framework could significantly impact breast cancer patient outcomes.