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Related Experiment Video

Updated: Mar 14, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

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XceptRf-Net: A Novel Deep Learning and Machine Learning Approach for Pneumonia Diagnosis.

Muhammad Usama Tanveer1, Kashif Munir1, Syed Ali Jafar Zaidi1

  • 1Institute of Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan.

Current Medical Imaging
|March 12, 2026
PubMed
Summary
This summary is machine-generated.

A novel hybrid deep learning and machine learning model, XceptRF-Net, accurately diagnoses pediatric pneumonia from chest X-rays. This interpretable framework combines Xception and Random Forest for enhanced clinical decision support.

Keywords:
Chest X-RaysDeep LearningMachine Learning.Pneumonia DiagnosisXceptRF-Net

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging Analysis
  • Computational Biology

Background:

  • Existing pneumonia diagnostic procedures have critical limitations.
  • Accurate initial diagnosis of pneumonia in pediatric patients is crucial.
  • There is a need for advanced and interpretable diagnostic frameworks.

Purpose of the Study:

  • To develop an advanced and interpretable diagnostic framework for pediatric pneumonia.
  • To combine deep learning and machine learning for high-accuracy initial diagnosis.
  • To overcome limitations of current diagnostic methods.

Main Methods:

  • Introduced XceptRF-Net, a hybrid model integrating Xception (deep feature learning) and Random Forest (probabilistic modeling).
  • Xception extracted high-level spatial features from pediatric chest X-rays.
  • Random Forest mapped features to a probabilistic space for robustness, tested with Logistic Regression, K-Nearest Neighbours, and Multi-Layer Perceptron.

Main Results:

  • The XceptRF-Net framework was evaluated on a dataset of 5,863 pediatric chest X-rays.
  • The model demonstrated benefits over conventional methods.
  • Logistic Regression achieved the highest diagnostic accuracy of 98%.

Conclusions:

  • The XceptRF-Net model's effectiveness validates combining deep feature extraction with probabilistic modeling.
  • The findings show the superiority of integrating convolutional deep features with ensemble learning for medical image analysis.
  • The proposed method offers a stable, explainable framework for clinical decision support in pediatric pneumonia screening.