Authors : Hansa S. Borse; Harshal M. Jadhav; Aryan A. Gaikwad; Mayank R. Lahare; Abhishek S. Parmar

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/5du7brtx

Scribd : https://tinyurl.com/3jswy8hz

DOI : https://doi.org/10.38124/ijisrt/26May1910

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Respiratory diseases such as COVID-19, pneumonia, asthma, bronchitis, and chronic obstructive pulmonary disease (COPD) require early diagnosis for effective treatment and healthcare support. Traditional diagnostic procedures are often expensive, time-consuming, and inaccessible in remote or resource-limited regions. This paper presents SpectroCough, an AI-powered respiratory disease screening framework that performs cough sound analysis using hybrid acoustic feature fusion and deep learning techniques. The proposed system supports both microphone-based cough recordings and stethoscopic respiratory recordings for flexible healthcare deployment. The framework performs audio preprocessing, Mel-spectrogram generation, MFCC extraction, handcrafted acoustic feature extraction, and fusion-based respiratory disease classification using convolutional neural networks and dense neural layers. Additional fake and noncough detection modules improve system reliability by filtering invalid respiratory sounds such as sneezing, breathing, and artificial cough patterns.

Keywords : Respiratory Disease Detection, Cough Analysis, Deep Learning, MFCC, Mel Spectrogram, Mobile Healthcare, Audio Classification.

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