Deep Learning Audio Spectrograms Processing to the Early COVID-19 Detection

Rodriguez C.R.; Angeles D.; Chafloque R.; Kaseng F.; Pandey B. · Proceedings - 2020 12th International Conference on Computational Intelligence and Communication Networks, CICN 2020 (2020) · DOI: 10.1109/cicn49253.2020.9242583

Resumen

The objective of the paper is to provide a model capable of serving as a basis for retraining a convolutional neural network that can be used to detect COVID-19 cases through spectrograms of coughing, sneezing and other respiratory sounds from infected people. To address this challenge, the methodology was focused on Deep Learning technics worked with a dataset of sounds of sick and non-sick people, and using ImageNet's Xception architecture to train the model to be presented through Fine-Tuning. The results obtained were a precision of 0.75 to 0.80, this being drastically affected by the quality of the dataset at our availability, however, when getting relatively high results for the conditions of the data used, we can conclude that the model can present much better results if it is working with a dataset specifically of respiratory sounds of COVID-19 cases with high quality.

Instituciones: Universidad Nacional Mayor de San Marcos; Universidad Nacional Federico Villarreal