Swarm Intelligent Metaheuristic Optimization Algorithms-Based Artificial Neural Network Models for Breast Cancer Diagnosis: Emerging Trends, Challenges and Future Research Directions

• Published in: Archives of computational methods in engineering Vol. 32; no. 1; pp. 381 - 398
• Main Authors: Veeranjaneyulu, K., Lakshmi, M., Janakiraman, Sengathir
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.01.2025; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial neural networks; Breast cancer; Classification; Computing time; Diagnosis; Engineering; Evaluation; Heuristic methods; Machine learning; Mathematical and Computational Engineering; Medical diagnosis; Neural networks; Optimization; Optimization algorithms; Review Article; Metaheuristic optimization algorithms; Deep learning model; Exploration; Breast cancer; Swarm intelligence; Neural network; Exploitation
• ISSN: 1134-3060; 1886-1784
• DOI: 10.1007/s11831-024-10142-2

Breast Cancer Disease is identified as one of the prime causes of death in women around the globe standing next to lung cancer. Breast cancer represents the development of malignant neoplasm from the breast cells. This breast cancer can be treated when it is identified at an early stage. Several researchers have contributed different machine learning approaches for maximizing the accuracy during the process of predicting breast cancer. Optimization of selected features is another important step essential for attaining maximized accuracy during the process of detection during the use of Artificial Neural Network. The utilization of optimization algorithm also helps in fine-tuning the hyperparameters of ANN such that the process of classification can be achieved with better precision and less computational time. In this paper, a Review on Swarm Intelligent metaheuristic optimization algorithms-based Artificial Neural Network-based Breast Cancer Diagnosis Schemes is presented for comparing different approaches depending on their efficacy in achieving the classification process. It presents the potentiality of wrapper and filter methods generally used for classifying cancer cells from normal cells. This review specifically concentrates on highlighting the significance of the swarm intelligent algorithms-based optimized ANN models which are contributed with its limitations. This review also demonstrates the future scope of research which could be concentrated from the identified extract of the literature. This review also highlighted the different kinds of evaluation metrics considered for assessing the potentiality of the existing ANN-based Breast Cancer Diagnosis Schemes with its need in utilization during evaluation.