Scholarlink

This study presents an approach to lung disease detection that merges neural network methodologies by using patient lung sounds alongside demographic information (location, age, and Body Mass Index (BMI)), instrument specifications, and indicators of crackles and wheezes. The objective of this paper is to offer an efficient, low cost diagnostic method. Traditional auscultation-based diagnosis faces challenges from subjectivity and sound variability. The proposed Respiratory Disease Detection Model (RDDM) uses spectrogram images from lung sounds to capture nuanced patterns while integrating clinical data for contextual insights. This fusion-based model provides automated analysis, enhancing diagnostic precision and efficiency, and ensuring good generalization to new cases, paving the way for advancements in lung disease detection.

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RDDM: A Respiratory Disease Detection Technique Using Deep Learning Models

  • 会议论文

  • 发表日期：

    2026年

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  • Tarun Meena,
  • Sujeet Kumar,
  • Abhishek Sharma,
  • Kakali Chatterjee,
  • Ashish Kumar

摘要

This study presents an approach to lung disease detection that merges neural network methodologies by using patient lung sounds alongside demographic information (location, age, and Body Mass Index (BMI)), instrument specifications, and indicators of crackles and wheezes. The objective of this paper is to offer an efficient, low cost diagnostic method. Traditional auscultation-based diagnosis faces challenges from subjectivity and sound variability. The proposed Respiratory Disease Detection Model (RDDM) uses spectrogram images from lung sounds to capture nuanced patterns while integrating clinical data for contextual insights. This fusion-based model provides automated analysis, enhancing diagnostic precision and efficiency, and ensuring good generalization to new cases, paving the way for advancements in lung disease detection.