Automated Detection of Pain Across Varied Intensity Levels Through the Fusion of CNN and Random Forest

An important part of preserving well-being is addressing pain, a complex human experience. In the medical field, accurate pain assessment is still difficult, especially when verbal communication is restricted. In response, by utilizing state-of-the-art technology, this research offers a strong and automated approach to pain identification. Feature extraction is done using Convolutional Neural Networks (CNNs), face expression analysis is done using deep learning techniques, and classification is done using Random Forest. When image preprocessing techniques are incorporated, data consistency and quality are improved, leading to consistent outcomes for a range of pain intensities. The system's ability to discriminate between various pain intensities, ranging from minor discomfort to excruciating anguish, is demonstrated by a carefully selected dataset. The accuracy and generalization abilities of the model are demonstrated by performance metrics such as precision, recall, and F1-Score. This study adds to the growing body of research on applications of computer vision in healthcare as well as the area of pain assessment. Promising developments in patient care, early intervention, and the comprehension of pain manifestations in many clinical contexts can be attributed to the computerized pain detection system.