Abstract
The integration of artificial intelligence (AI) and the Internet of Things (IoT) has tremendous prospects in smart healthcare. The advancement of AI in the form of deep learning brought a revolution in automatic classification and detection systems. In addition, next-generation wireless communications such as 5G networking brought speed and the seamless transmission of data. With the convergence of these elements, the smart healthcare sector is currently booming. Particularly during the post-COVID-19 pandemic, the necessity of smart healthcare has come to light more than before. A significant number of people suffer from voice pathology. This pathology can be easily cured if detected early. In this study, a voice pathology detection system within a smart healthcare framework is proposed. The inputs are obtained by the IoT, namely microphones and electroglottography (EGG) devices to capture voice and EGG signals, respectively. Spectrograms are obtained from these signals and fed into a pretrained convolutional neural network (CNN). The features extracted from the CNN are fused and processed using a bi-directional long short-term memory network. The proposed system is evaluated using a publicly available database, called the Saarbruecken voice database. The experimental results show that bimodal input performs better than a single input. An accuracy of 95.65% is obtained for the proposed system.