ENHANCING INFANT PAIN DETECTION WITH HYBRID ATTENTION MECHANISMS IN LIGHTWEIGHT MOBILENETV3 ARCHITECTURES - Pravitasari, Anindya Apriliyanti; Hendrawati, Triyani; Chadidjah, Anna; Herawan, Tutut (Malaysian Journal of Computer Science, 2025)
Creating an automated pain detection system for infants less than a year old is essential because they are unable to communicate their discomfort verbally. Conventional assessment techniques like FLACC (Face, Legs, Activity, Cry, Consolability) require considerable time and may not be effective for infants with vocal cord impairments. Utilizing infants’ facial expressions for real-time, automated pain detection presents a promising approach that facilitates rapid medical response. This study adopts a machine learning approach using infant facial expressions as input and explores the efficacy of various MobileNetV3 architectures, both Small and Large, enhanced with attention mechanisms. We introduced modifications involving 12 model variants, including the integration of CBAM (Convolutional Block Attention Module), ECA (Efficient Channel Attention), and SAM (Spatial Attention Module) attention modules, as well as hybrid attention configurations (ECA + CBAM and ECA + SAM). Training was conducted on a FLACC-based dataset comprising 56 videos collected from infants under 12 months undergoing hernia treatment at Dr. Soetomo General Hospital, Surabaya, East Java, Indonesia, from November 2011 to December 2022. The dataset is categorized into three pain levels: no pain, low/moderate pain, and severe pain. Results demonstrate that attention mechanisms significantly enhance model accuracy, with hybrid configurations consistently achieving the best performance. The ECA + CBAM hybrid configuration achieved the highest accuracy of 94.5%, representing a 5% improvement over baseline models, while also reducing misclassifications across all pain levels. However, these gains come with increased computational complexity, including higher parameter counts, greater FLOPs, longer inference times, and higher memory usage. These results indicating their robustness in real-time pain detection for infants, thereby highlighting their potential for practical clinical applications.