Optimizing cancer classification: A metaheuristic-driven review of feature selection and deep learning approaches

Abstract

Cancer remains a leading cause of mortality, where early detection significantly improves survival rates. Advances in technology have enabled automated cancer detection using medical imaging and microarray gene expression data. However, these datasets often contain redundant or noisy features that hinder classification performance. Feature selection is key preprocessing step to enhance accuracy and reduce computational costs. In cancer-related medical research, optimizing deep learning architectures is crucial for better classification outcomes. Metaheuristic algorithms have been popular for tackling both feature selection and deep neural networks (DNN) optimization. This survey reviews 91 peer-reviewed articles (2012–2025) on metaheuristics for feature selection and DNN optimization in cancer classification using medical images and microarray data. Literature was sourced from databases such as Google Scholar, IEEE Xplore, Elsevier, ResearchGate, Springer, MDPI, and ScienceDirect. Our findings indicate that k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN) are the most widely adopted classifiers, used in 23%, 21%, and 18% of cases, respectively. Among metaheuristics, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO) dominate the landscape, appearing in 13%, 11%, and 10% of studies. We also review 39 image-based and 44 microarray cancer datasets. This survey identifies critical gaps in current research and proposes several future directions to enhance model robustness and classification accuracy. Through a detailed comparative analysis, this study provides valuable insights for researchers and decision-makers, highlighting the need for continued innovation in computational methods for cancer detection and diagnosis.