Abstract
The primary driver of female death remains breast cancer, highlighting the need for effective screening and accessible diagnostic tools. Digital hardware approaches have demonstrated strong performance but are constrained by high computational and energy demands, limiting their use in real-time portable devices. Analog Artificial Neural Networks (AANNs) offer advantages in speed, power efficiency, and compact hardware, though they remain experimental. This survey reviews AANNs for breast cancer diagnosis, applying a structured methodology to identify and compare studies across architectures, hardware technologies, accuracy, power consumption, and clinical applicability. A framework is proposed to organize the field and examine the dataset. The survey highlights sensitivity to environmental factors as a design challenge, while moderating clinical claims by discussing pathways and deployment barriers. By synthesizing current research, this survey motivates the development of clinically validated solutions that, if attained, could advance medical informatics by enabling the integration of analog models into clinical practice.
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