A Systematic Literature Review of Convolutional Block Attention Module (CBAM) in Image Classification - Vincentius Bernando Wijaya; Joko Siswantoro (Teknika, 2026)
Image classification has become one of the most important tasks in computer vision and has been widely applied in medical imaging, agriculture, surveillance, and industrial systems. Recently, the Convolutional Block Attention Module (CBAM) has attracted significant attention for improving feature representation through channel and spatial attention mechanisms. However, studies on CBAM remain scattered across application domains, backbone architectures, deployment strategies, and evaluation approaches, making its implementation and effectiveness difficult to comprehensively understand. Therefore, this study conducted a Systematic Literature Review (SLR) based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to analyze CBAM implementation in image classification studies published between 2018 and 2025. The literature search used Google Scholar through the Publish or Perish application. Of 855 identified articles, 47 met the eligibility criteria and were included. The review analyzed research domains, backbone architectures, CBAM effectiveness, deployment positions, evaluation metrics, and cross-analytical relationships. Medical Imaging was the dominant application domain, while ResNet was the most frequently used backbone architecture. Analysis of 156 ablation experiments showed that 82.1% reported performance improvements after CBAM integration, whereas 91.5% of the reviewed studies demonstrated positive outcomes. Cross-analysis further indicated that CBAM was particularly effective for fine-grained visual recognition tasks, most commonly integrated within backbone architectures, and generally more compatible with residual-based than lightweight architectures. Furthermore, computational efficiency remained underreported. These findings provide a comprehensive synthesis of current CBAM research and practical guidance for future image classification studies.