Classification of Cancer Microarray Data Based on Deep Learning: A Review

Abstract

 This review article delves into applying deep learning methodologies in conjunction with microarray data for cancer classification. The study provides a comprehensive overview of recent advancements in utilizing deep learning techniques to accurately categorize cancer types based on intricate patterns discerned from microarray datasets. Various aspects are covered, including integrating deep learning algorithms, exploring diverse cancer types, and analyzing microarray data to enhance classification accuracy. The review synthesizes findings from recent research, highlighting the efficacy of deep learning in uncovering subtle and complex relationships within microarray data that contribute to improved classification outcomes. Key insights into the strengths and limitations of employing deep learning in this context are discussed, offering a critical appraisal of the field's current state. This review aims to provide a valuable resource for researchers, clinicians, and practitioners interested in cutting-edge developments in cancer classification methodologies by exploring the intersection of deep learning and microarray technology. The synthesis of knowledge presented herein contributes to a deeper understanding of the potential and challenges associated with harnessing deep learning for enhanced classification accuracy in the realm of cancer research.