Malak Ghourabi

Coughing, a common symptom associated with various respiratory problems, is a crucial indicator for diagnosing and tracking respiratory diseases. Accurate identification and categorization of cough sounds, specially distinguishing between wet and dry coughs, are essential for understanding underlying health conditions. This research focuses on applying the Swin Transformer for classifying wet and dry coughs using short-time Fourier transform (STFT) representations. We conduct a comprehensive evaluation, including a performance comparison with a 2D convolutional neural network (2D CNN) model, and exploration of two distinct image augmentation methods: time mask augmentation and classical image augmentation techniques. Extensive hyperparameter tuning is performed to optimize the Swin Transformer’s performance, considering input size, patch size, embedding size, number of epochs, optimizer type, and regularization technique. Our results demonstrate the Swin Transformer’s superior accuracy, particularly when trained on classically augmented STFT images with optimized settings (320 × 320 input size, RMS optimizer, 8 × 8 patch size, and an embedding size of 128). The approach achieves remarkable testing accuracy (88.37%) and ROC AUC values (94.88%) on the challenging crowdsourced COUGHVID dataset, marking improvements of approximately 2.5% and 11% increases in testing accuracy and ROC AUC values, respectively, compared to previous studies. These findings underscore the efficacy of Swin Transformer architectures in disease detection and healthcare classification problems.

Early detection of physical frailty and infectious diseases in seniors is important to avoid any fatal drawback and promptly provide them with the necessary healthcare. One of the major symptoms of viral infections is elevated body temperature. In this work, preparation and implementation of multi-age thermal faces dataset is done to train different “You Only Look Once” (YOLO) object detection models (YOLOv5,6 and 7) for eye detection. Eye detection allows scanning for the most accurate temperature in the face, which is the inner canthus temperature. An approach using an elderly thermal dataset is performed in order to produce an eye detection model specifically for elderly people. An application of transfer learning is applied from a multi-age YOLOv7 model to an elderly YOLOv7 model. The comparison of speed, accuracy, and size between the trained models shows that the YOLOv7 model performed the best (Mean average precision at Intersection over Union of 0.5 (mAP@.5) = 0.996 and Frames per Seconds (FPS) = 150). The bounding box of eyes is scanned for the highest temperature, resulting in a normalized error distance of 0.03. This work presents a fast and reliable temperature detection model generated using non-contact infrared camera and a deep learning approach.