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Related Concept Videos

Classification of Illness01:17

Classification of Illness

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
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Related Experiment Video

Updated: Jan 16, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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A Deep Learning-Based EffConvNeXt Model for Automatic Classification of Cystic Bronchiectasis: An Explainable AI

Veysi Tekin1, Muhammed Tekinhatun2, Salih Taha Alperen Özçelik3

  • 1Department of Chest Diseases, Faculty of Medicine, Dicle University, Diyarbakır, Turkey.

Journal of Imaging Informatics in Medicine
|September 25, 2025
PubMed
Summary
This summary is machine-generated.

A new deep learning model, EffConvNeXt, accurately distinguishes cystic bronchiectasis and pneumonia on chest X-rays. This hybrid model combines EfficientNetB1 and ConvNeXtTiny, improving diagnostic accuracy for critical respiratory conditions.

Keywords:
Chest X-rayConvNeXtTinyCystic bronchiectasisEfficientNetB1Pneumonia

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Respiratory Medicine

Background:

  • Cystic bronchiectasis and pneumonia are significant global health concerns impacting morbidity and mortality.
  • Accurate and timely diagnosis of these respiratory conditions is essential for improving patient outcomes.
  • Overlapping features on chest X-rays (CXRs) present diagnostic challenges, necessitating advanced analytical tools.

Purpose of the Study:

  • To develop and evaluate a novel deep learning model, EffConvNeXt, for enhanced classification of cystic bronchiectasis, pneumonia, and normal cases from CXRs.
  • To leverage a hybrid approach combining EfficientNetB1 and ConvNeXtTiny to improve diagnostic accuracy and efficiency in medical image analysis.
  • To address the limitations of individual deep learning models by integrating their strengths for superior performance in CXR interpretation.

Main Methods:

  • The study proposed the EffConvNeXt model, a hybrid architecture integrating EfficientNetB1 and ConvNeXtTiny.
  • The model was trained and validated using a dataset of 5899 CXR images from Dicle University Medical Faculty.
  • Performance was evaluated by comparing EffConvNeXt against individual models (ConvNeXtTiny and EfficientNetB1) and other deep learning models.

Main Results:

  • The individual ConvNeXtTiny model achieved 97.12% accuracy, and EfficientNetB1 achieved 97.79% accuracy.
  • The proposed EffConvNeXt model demonstrated a superior accuracy rate of 98.25%, representing a 0.46% improvement over the best individual model.
  • EffConvNeXt outperformed other tested deep learning models in classifying CXR images for cystic bronchiectasis and pneumonia.

Conclusions:

  • The EffConvNeXt model offers a reliable and automated solution for differentiating cystic bronchiectasis and pneumonia on CXRs.
  • The hybrid deep learning approach enhances diagnostic accuracy, supporting clinical decision-making in respiratory disease diagnosis.
  • This advanced model shows significant potential for rapid and precise analysis of medical imaging in clinical settings.