CapsuleThermNet: A CNN-CapsuleNet Architecture for Early Detection of Suicide Risk in Depressed Patients Using Thermal Facial Imaging - Eddy Muntina Dharma; Harjanto Prabowo; Agung Trisetyarso; Tjhin Wiguna (IEEE Access, 2025)
Early detection of suicide risk plays a vital role in improving mental health care outcomes, especially for individuals suffering from depression. Traditional methods, such as clinical interviews, are widely regarded as the gold standard in suicide risk assessment. However, these methods often rely heavily on verbal communication and subjective interpretation, which may lead to the overlooking of important non-verbal cues that could indicate distress. To address this limitation, this study proposes a novel approach to early suicide risk detection by utilizing thermal facial imaging combined with a deep learning model called CapsuleThermNet. CapsuleThermNet is designed by integrating CNN with Capsule Networks (CapsNet). In this architecture, CNNs are responsible for extracting spatial features from thermal facial images, while CapsNet captures hierarchical relationships and performs classification tasks. The study uses a specially developed primary dataset, known as the FaceTherm Suicide Risk (FTSR) Dataset, which includes 152 thermal images of depressed patients. These images are categorized equally into two groups: patients identified as at risk of suicide (76 images) and those not at risk (76 images). The proposed model achieves excellent performance with an accuracy of 99.67%, precision of 93.75%, sensitivity of 100%, specificity of 93.33%, F1-score of 96.77%, and AUC of 0.97. The perfect recall rate highlights the model’s strength in minimizing false negatives—crucial in clinical settings. With its robust classification capabilities, CapsuleThermNet holds promise as a reliable decision-support tool for psychiatrists, enabling earlier detection and timely intervention for patients at risk of suicide before proceeding to more extensive clinical evaluation.