Koagne Longpa T. Silas1*, Djimeli-Tsajio Alain B1,2*, Fotsing Talla Bernard1,3, Lienou T. Jean- Pierre1,3 and Geh Wilson Ejuh4,5
1Research Unit of Automation and Applied Computer Science URAIA, IUT-FV, University of Dschang, P.O. Box 134, Bandjoun, Cameroon
2Department of Telecommunication and Network Engineering, IUT-FV, University of Dschang, P.O. Box 134, Bandjoun, Cameroon
3Department of Computer Engineering, IUT-FV, University of Dschang, P.O. Box 134, Bandjoun, Cameroon
4Department of General and Scientific Studies, IUT-FV, University of Dschang, P.O. Box 134, Bandjoun, Cameroon
5Department of Electrical and Electronic Engineering, National Higher Polytechnic Institute, University of Bamenda, P. O. Box 39, Bambili, Cameroon

*Corresponding author: Koagne Longpa T. Silas, Research Unit of Automation and Applied Computer Science URAIA, IUT-FV, University of Dschang, P.O. Box 134, Bandjoun, Cameroon, E-mail: silas.koagne@univ-dschang.org; ORCID: https://orcid.org/0009-0006-5061-1821
Djimeli-Tsajio Alain B, Department of Telecommunication and Network Engineering, IUT-FV, University of Dschang, P.O. Box 134, Bandjoun, Cameroon, E-mail: alain.djimeli@univ-dschang.org; ORCID: https://orcid.org/0000-0002-4433-0074

Citation: T Silas KL, Alain BDT, Bernard FT, Jean-Pierre LT, Ejuh GW. Breast Cancer Diagnosis with Analog Artificial Neural Network: A Survey of Architectures, Implementations, and Challenges. Health AI Front. 2026;1(1):1-26.

Received Date: Jan 12, 2026; Accepted Date: Mar 23, 2026; Published Date: Mar 27, 2026

Original Research Article

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.

Keywords: Breast cancer diagnosis; Analog artificial neural network; Multilayer perceptron; Complementary metal-oxide semiconductor; Very large-scale integration