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Articles

Generative AI to Predict Breast Cancer: Current Approaches, Advancements, and Challenges

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  • Alma Mohapatra
Alma Mohapatra
AIML and Generative AI
Published in : International Journal of Scientific Research and Management (IJSRM) , Vol. 12 No. 11 (2024)

Submission to VIJ 2024-11-08

Keywords

  • Generative AI, Breast Cancer Prediction, Medical Imaging, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Multi-Modal Data Integration, Healthcare AI, Predictive Modeling, Medical Image Analysis

Abstract

Breast cancer is one of the most prevalent cancers worldwide, with early detection playing a critical role in improving patient outcomes and survival rates. Traditional diagnostic methods, though effective, often face challenges in terms of accessibility, cost, and the need for highly skilled radiologists. Recent advancements in Artificial Intelligence (AI), particularly Generative AI (GAI), have opened new avenues in medical imaging and predictive analysis. Unlike conventional AI models, Generative AI can produce synthetic data that mimics real mammograms, providing a robust solution to data scarcity and enhancing model training.

This paper explores the application of Generative AI, especially Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), in predicting breast cancer through synthetic imaging and data augmentation techniques. By generating high-quality synthetic breast cancer imaging data, GAI models can improve diagnostic accuracy and sensitivity. We review recent studies and case examples where Generative AI has demonstrated efficacy in predicting and detecting breast cancer at early stages, often outperforming traditional AI models. In addition, we provide a technical overview of the workflow involved in training and deploying GAI models for breast cancer prediction, highlighting steps from data acquisition and preprocessing to model evaluation.

The findings suggest that while Generative AI holds significant promise in predictive oncology, it also faces challenges related to model interpretability, data bias, and ethical considerations. We discuss these limitations and propose strategies to address them, focusing on the need for diversified data sources, model transparency, and collaboration between data scientists and healthcare professionals. The paper concludes with an outlook on future advancements in Generative AI, including the integration of newer models such as diffusion models, and emphasizes the potential of these technologies to revolutionize cancer diagnostics by providing cost-effective, accessible, and highly accurate predictive tools for breast cancer.

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