Leveraging an Optimized Deep Convolutional Neural Network Architecture for the Automated Diagnosis of Pathological Conditions Through Acoustic Analysis of Human Cough Signatures

The usage of artificial intelligence (AI) techniques particularly deep learning algorithms, has recently increased dramatically and the healthcare sector is only one of many industries affected Using deep convolutional neural networks (CNNs) enhanced with human auditory inputs we present a novel approach to disease detection in this paper The proposed system aims to provide an effective economical and non invasive method of early disease detection we employ a deep convolutional neural network (CNN) architecture that has been optimized for audio input processing. Further more we use data augmentation methodologies to strengthen the model's capacity to generalize and handle varied patient groups and diseases more effectively. Our all encompassing approach incorporates denoising techniques using Wavelet transform denoising segmentation via K-mean clustering, feature extraction using Recursive Feature Elimination (RFE) and classification using Enhanced Deep Convolutional Neural Networks (EDCNN) which improves the accuracy and dependability of the system in identifying diseases based on human sound signatures.