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

Mahsa Abbasi, Fatemeh Imani, Ali Ghaffari, Amir Seyyedabbasi

Abstract read

In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Mahsa AbbasiDepartment of Computer Engineering, Ta.C, Islamic Azad University, Tabriz, Iran.

Fatemeh ImaniDepartment of Computer Engineering, Ta.C, Islamic Azad University, Tabriz, Iran. f.imani661@iau.ac.ir.

Ali GhaffariDepartment of Computer Engineering, Ta.C, Islamic Azad University, Tabriz, Iran. A.ghaffari@iaut.ac.ir.

Amir SeyyedabbasiDepartment of Computer Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The traditional stethoscope auscultation used for the detection of lung diseases is hampered by its poor sensitivity, complexity of sound, and dependence on clinical competence. As a result, it frequently leads to diagnostic errors, treatment delays, and inaccessibility in settings with limited resources. These difficulties highlight the need for automated, inexpensive, and portable diagnostics. This paper presents the lightweight two-layer 1D-CNN (one-dimensional convolutional neural network) for lung illness classification called LDSC. The signals are processed using normalized MFCC (Mel-Frequency Cepstral Coefficients) features after being resampled to 4 kHz, enhanced (noise, time-stretch, pitch-shift), and divided into 3-second frames. According to simulation results, the suggested scheme achieves 98% accuracy, sensitivity, and specificity on the ICBHI (International Conference on Biomedical and Health Informatics) dataset and 99% on the KAUH (King Abdullah University Hospital) dataset, respectively.

Indexed as

AuscultationDiagnosis, Computer-AssistedLung DiseasesNeural Networks, ComputerAlgorithmsHumansSensitivity and SpecificitySignal Processing, Computer-Assisted1D-CNNBinary classificationDeep learningLung diseaseMulti-class classification

Identifiers

PMID41495392

DOI10.1038/s41598-025-34485-3

PMCPMC12858887

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.