Automated Diagnosis of Breast Cancer Using Mammograms

Introduction

Breast cancer is one of the most prevalent cancers affecting women worldwide. Early detection through mammography significantly improves treatment outcomes. This project proposal aims to develop an automated system for diagnosing breast cancer using mammograms, inspired by recent advancements in machine learning and image processing.

Background

Recent research has demonstrated the potential of machine learning in medical imaging, particularly in enhancing the accuracy of breast cancer diagnosis from mammograms. Techniques such as convolutional neural networks (CNNs) have been successfully applied to detect and classify abnormalities in mammographic images. These methods can assist radiologists by providing a second opinion and reducing diagnostic errors.

Project Objective

The primary objective of this project is to develop a machine learning-based system that can automatically diagnose breast cancer from mammograms. The system aims to improve diagnostic accuracy and efficiency, ultimately aiding in early detection and treatment planning.

Methodology

1. Data Collection and Preprocessing

• Datasets: Utilize publicly available datasets such as the Digital Database for Screening Mammography (DDSM) for training and evaluation.
• Image Preprocessing: Perform preprocessing steps such as normalization, augmentation, and noise reduction to enhance image quality and model performance.

2. Model Architecture

• Convolutional Neural Networks (CNNs): Implement CNNs for feature extraction from mammogram images.
• Classification Layer: Use fully connected layers for classifying images into benign or malignant categories.

3. Training and Evaluation

• Training: Employ techniques such as transfer learning to improve model training with limited data.
• Evaluation Metrics: Measure performance using metrics like accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve.

Expected Outcomes

The proposed system is expected to achieve high accuracy in diagnosing breast cancer from mammograms, providing reliable support to radiologists. By leveraging advanced machine learning techniques, the system should effectively differentiate between benign and malignant cases with minimal false positives.

Conclusion

This project aims to enhance breast cancer diagnosis through an automated system using mammograms. The integration of CNNs and robust preprocessing techniques is anticipated to significantly improve diagnostic accuracy, contributing to better patient outcomes.

For further details on related research, please refer to the paper "Automated Diagnosis of Breast Cancer Using Mammograms," available at https://ieeexplore.ieee.org/document/8489208.

For dataset access, please refer to the Digital Database for Screening Mammography (DDSM) available at http://www.eng.usf.edu/cvprg/Mammography/Database.html.