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An anatomically enhanced and clinically validated framework for lung abnormality classification using deep features

Suresh Kumar Samarla1,2, Maragathavalli P1

  • 1IT, Puducherry Technological University, Puducherry, India.

Methodsx
|June 9, 2025
PubMed
Summary
This summary is machine-generated.

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The lungs are a pair of vital organs connected to the trachea via the left and right bronchi. The base of these organs meets the dome-shaped muscle known as the diaphragm. Encased by the pleurae, the lungs contact the mediastinum. The right lung is shorter yet wider, and has a larger volume than the left lung. The left lung has an indentation known as the cardiac notch. The superior region of the lungs is referred to as the apex, whereas the base is the lower region near the diaphragm. The...
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This study introduces a novel two-stage framework for detecting lung abnormalities in X-rays. The method accurately classifies pneumonia and its subtypes while preserving anatomical integrity and enhancing diagnostic visibility.

Area of Science:

  • Medical Imaging
  • Radiology
  • Computer-Aided Diagnosis

Background:

  • Chest X-rays are crucial for diagnosing lung abnormalities, but subtle variations can be missed by traditional methods.
  • Image augmentation techniques can distort anatomical features, leading to misdiagnosis.
  • Accurate and efficient classification of lung conditions is essential for timely treatment.

Purpose of the Study:

  • To propose a novel two-stage framework, Anatomical Segmentation and Color-Based Enhancement (ASCE), for precise lung abnormality classification.
  • To preserve anatomical integrity during the detection process.
  • To improve the efficiency and interpretability of lung abnormality diagnosis.

Main Methods:

  • A two-stage framework integrating Anatomy-Preserved Segmentation and Color-Based Enhancement.
Keywords:
Anatomical Segmentation and Color-Based EnhancementAnatomical segmentationChest X-raysColor based enhancementDeep learningKL divergenceLung AbnormalityPneumonia Detection

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  • Utilizing Kullback-Leibler (KL) divergence to quantify deviations from healthy lung regions.
  • A lightweight pipeline for computational efficiency (approximately 0.06s/image).

    • Main Results:

    • Stage 1 achieved 95% accuracy, 0.98 AUC, and 0.92 F1-score for Normal vs. Pneumonia classification.
    • Stage 2 achieved 100% accuracy and F1-score for distinguishing Viral and Bacterial pneumonia subtypes.
    • The method preserves diagnostic features and enhances visibility of abnormalities.

    Conclusions:

    • The ASCE framework offers precise and efficient classification of lung abnormalities.
    • The approach enhances diagnostic visibility and enables quantitative analysis.
    • This method ensures clinical interpretability by preserving critical anatomical structures.