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

Abstract

Detecting lung abnormalities via chest X-rays is challenging due to understated tissue variations often ignored by traditional methods. Augmentation techniques like rotation or flipping risk distorting critical anatomical features, actually leading to misdiagnosis. This paper proposes a novel two-stage ASCE (Anatomical Segmentation and Color-Based Enhancement) framework for precise and efficient classification of lung abnormalities while preserving anatomical integrity.

Stage 1 classifies Normal vs. Pneumonia with 95 % accuracy, an AUC of 0.98, and an F1-score of 0.92. Stage 2 distinguishes Pneumonia into Viral and Bacterial subtypes with 100 % accuracy and F1-score. This approach integrates segmentation and tissue-specific color enhancements with Kullback-Leibler (KL) divergence, quantifying deviations from healthy lung regions for improved classification. The lightweight pipeline ensures computational efficiency (∼0.06s/image) and clinical interpretability by preserving diagnostic features, enhancing visibility, and enabling quantitative analysis.

• 1.
  Preserving Anatomical Structures: The methodology ensures that diagnostic features are preserved and highlighted with Anatomy-Preserved Segmentation
• 2.
  Enhancing Diagnostic Visibility: The system employs targeted colour-based enhancement that improves the visibility of potential abnormalities
• 3.
  Quantitative Analysis with Kullback-Leibler (KL) divergence: The model enhances precise identification of abnormal tissue by comparing the probability distributions of healthy lungs and abnormal areas