Real-time mask-wearing detection in video streams using deep convolutional neural networks for face recognition

Abstract

This research aims to develop a real-time mask-wearing detection system using deep convolutional neural networks (CNNs). This is crucial in the coronavirus disease 2019 (COVID-19) pandemic to alert individuals who are not wearing masks early on, thereby reducing the spread of the virus. Since COVID-19 primarily spreads through respiratory droplets and mask-wearing is recommended, our proposed study utilizes computer vision techniques, specifically image processing, to detect masked and unmasked faces. We employ a customized CNN architecture consisting of five convolutional layers, followed by max-pooling layers and fully connected (FC) layers. The final output layer utilizes softmax activation for classification. The model is updated with optimized layer configurations and parameter values. We are developing an application that uses a digital camera as an input device. The application utilizes a dataset comprising 11,792 image samples, which are used for training and testing purposes with the 80:20 ratio. Real-time testing is conducted using human subjects captured by the camera. The experimental results demonstrate that the CNN method achieves a classification accuracy of 99% on the training data and 98.83% during real-time video testing. These findings suggest that the real-time mask detection system using CNN performs effectively.

A deep learning framework for accurate diagnosis of colorectal cancer using histological images

“…These CNNs have demonstrated successful applications in various deeplearning tasks, particularly in predicting and diagnosing colorectal cancer at an early stage. In this study, these CNN models have shown promising results and outperformed other approaches [7], [8].…”

“…Additionally, the confusion matrix components are employed to calculate important metrics like accuracy (ACC), precision positive predictive value (PPV), sensitivity (Recall), specificity (SPC), and F1-score. − Accuracy is the ratio of correctly predicted values to all predicted values, according to (7).…”