Artificial Intelligence Techniques for IoT-Based Breast Cancer Detection with Bayesian Quantized Neural Networks Using Energy-Efficient WSN: Trends and Challenges

Abstract

Breast cancer remains one of the leading causes of mortality among women worldwide, making early and accurate detection essential for improving survival rates. The integration of Artificial Intelligence, Internet of Things (IoT), and Wireless Sensor Networks (WSN) has significantly enhanced real-time monitoring and diagnosis of breast cancer. This paper presents a comprehensive review of AI-driven techniques for breast cancer detection using IoT-based systems, Bayesian quantized neural networks, and energy-efficient WSN frameworks. Recent studies demonstrate that deep learning models such as Convolutional Neural Networks and Bayesian Neural Networks have significantly improved diagnostic accuracy by extracting complex features from medical imaging data. Bayesian neural networks, in particular, provide uncertainty estimation, enhancing decision-making reliability in clinical applications. Furthermore, IoT-based healthcare systems enable continuous monitoring and real-time data transmission through distributed sensor networks, improving accessibility and efficiency in remote healthcare environments. Energy efficiency remains a critical concern in WSN-based healthcare systems. Advanced routing algorithms and lightweight AI models have been proposed to minimize power consumption while maintaining performance. Additionally, hybrid models integrating AI with blockchain and edge computing enhance data security and reduce latency in IoT environments. This review focuses on recent advancements, highlighting emerging trends such as Bayesian learning, quantized neural networks, and energy-aware communication protocols. It also discusses challenges including computational complexity, scalability, and real-time implementation. The integration of AI, IoT, and WSN technologies is expected to play a crucial role in developing next-generation intelligent healthcare systems.