AI-Powered Detection: Implementing Deep Learning for Breast Cancer Prediction

Anmol Tanveer; Saima Munawar; Nasir Naveed

Authors

Anmol Tanveer Faculty of Computer Science and Information Technology, Virtual University of Pakistan, Lahore
Saima Munawar Faculty of Computer Science and Information Technology, Virtual University of Pakistan, Lahore https://orcid.org/0000-0002-2446-3670
Nasir Naveed Faculty of Computer Science and Information Technology, Virtual University of Pakistan, Lahore

Keywords:

Breast Cancer, Deep Learning, Artificial Intelligence, DenseNet121 Model, Mammography Images

Abstract

Beast cancer remains a critical global health issue, affecting millions of women worldwide. According to the World Health Organization (WHO), there were 2.3 million new cases and 685,000 deaths from breast cancer in 2020 alone. This makes breast cancer the most prevalent cancer globally, with 7.8 million cases diagnosed over the past five years. As the prevalence of breast cancer continues to rise, the need for accurate and efficient diagnostic tools becomes increasingly urgent. Artificial Intelligence (AI) has shown considerable promise in enhancing breast cancer detection and diagnosis. Over the past two decades, AI tools have increasingly aided physicians in interpreting mammograms, offering the potential for automated, precise, and early cancer detection. However, significant challenges remain, particularly concerning data imbalance in datasets—where cancerous images are often underrepresented—and the issue of low pixel resolution, which can obscure crucial details in medical images. This work utilizes a subset of the data called Mini-DDSM, a lightweight version of the Digital Database for Screening Mammography. To address these challenges, our research employed the Neighborhood Cleaning Rule (NCR) algorithm from the imbalance library, designed to mitigate data imbalance by refining the dataset through the selective removal of noisy and borderline examples. This method enhances the quality of training data, enabling AI models to learn more effectively. We developed a deep learning model that incorporates a transfer learning layer (DenseNet121), dense layers, a global pooling layer, and a dropout layer to optimize performance. This model demonstrated promising results, effectively addressing the challenges of data imbalance and low image resolution. Our approach underscores the potential of AI to significantly improve breast cancer detection and diagnosis, ultimately leading to better patient outcomes. Continued research and refinement of AI techniques will be crucial in overcoming remaining challenges and fully realizing the potential of these technologies in healthcare.

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