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Related Experiment Video

Updated: Jan 13, 2026

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LDSC: enhancing lung disease diagnosis using a simple 1D-CNN.

Mahsa Abbasi¹, Fatemeh Imani², Ali Ghaffari^3,4,5

¹Department of Computer Engineering, Ta.C, Islamic Azad University, Tabriz, Iran.

Scientific Reports

|January 6, 2026

Summary

This study introduces a new AI tool for diagnosing lung diseases using sound analysis. The lightweight deep learning model achieves high accuracy, offering a promising alternative to traditional stethoscopes for accessible respiratory diagnostics.

Keywords:

1D-CNN Binary classification Deep learning Lung disease Multi-class classification

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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Area of Science:

Biomedical Engineering
Artificial Intelligence in Medicine
Respiratory Medicine

Background:

Traditional stethoscope auscultation for lung disease detection suffers from low sensitivity, complex acoustics, and reliance on clinician expertise.
These limitations contribute to diagnostic errors, treatment delays, and accessibility issues, particularly in resource-limited settings.
There is a critical need for automated, affordable, and portable diagnostic solutions for lung illnesses.

Purpose of the Study:

To develop and evaluate a lightweight deep learning model for automated lung illness classification.
To assess the performance of the proposed model using Mel-Frequency Cepstral Coefficients (MFCC) features derived from respiratory sounds.

Main Methods:

Respiratory sound signals were resampled to 4 kHz, enhanced (noise reduction, time-stretch, pitch-shift), and segmented into 3-second frames.
Normalized Mel-Frequency Cepstral Coefficients (MFCC) were extracted as features from the processed signals.
A lightweight two-layer one-dimensional convolutional neural network (1D-CNN), termed LDSC, was designed for lung illness classification.

Main Results:

The LDSC model achieved 98% accuracy, sensitivity, and specificity on the International Conference on Biomedical and Health Informatics (ICBHI) dataset.
The model demonstrated even higher performance on the King Abdullah University Hospital (KAUH) dataset, reaching 99% accuracy, sensitivity, and specificity.
These results indicate robust performance in classifying lung illnesses based on acoustic features.

Conclusions:

The proposed lightweight 1D-CNN model (LDSC) offers a highly accurate and efficient method for automated lung illness classification.
The approach using MFCC features shows significant potential for developing inexpensive, portable, and reliable respiratory diagnostic tools.
This technology could improve diagnostic accuracy and accessibility, especially in underserved healthcare environments.