A Deep Learning Approach to Improving Patient Safety in Healthcare Using Real-Time Face Mask Detection

Face masks are frequently used during health an emergency, which emphasizes the need for efficient mask detection systems to guarantee public safety and adherence to health laws. The goal of this research is to accurately identify between masked and unmasked human faces using a deep learning-based approach. By employing a blend of convolutional neural networks (CNNs), the research makes use of large-scale picture datasets to train the model and improve its performance in various scenarios. The model is a useful tool for automated surveillance and public health monitoring systems, as evidenced by experimental results that show its robustness in reaching high accuracy rates. The proposed work explore by using MobileN etV2 within a Python environment. The next stage in the process is pre-processing the data, which often entails normalizing pixel values and scaling images to the input size that MobileNetV2 expects (224×224 pixels, for example).