PneumoSense: A Pneumonia detection model using Cough Sound analysis

Pneumonia, a respiratory infection with significant global mortality rates, can be effectively screened using non-invasive methods like cough sound analysis. This study presents a deep learning-based classification model called ”PneumoSense”, leveraging a one-dimensional Convolutional Neural Network (1D-CNN), Bidirectional Gated Recurrent Units (Bi-GRU), and an Attention mechanism to detect pneumonia from cough audio signals. PneumoSense is trained on a Coswara dataset comprising 270 healthy and 82 pneumonia cough samples, with data augmentation techniques applied to improve generalization and model robustness. The proposed method achieved a test accuracy of 97.00 %, indicating its potential as a viable screening tool for respiratory illnesses using simple audio input.