Optimizing Radiographic Diagnosis Through Signal-Balanced Convolutional Models

TL;DR

This paper introduces a deep learning framework that improves chest X-ray diagnosis by combining signal fidelity analysis with transfer learning, achieving high accuracy and transparency.

Contribution

A novel explainable deep learning framework integrating signal fidelity and transfer learning for reliable and transparent medical image diagnosis.

Findings

ResNet-50 achieved the highest classification accuracy (93.7%) and macro-AUC = 0.97 on a chest X-ray dataset.

EfficientNetB3 demonstrated superior generalization with reduced parameter overhead.

Grad-CAM visualizations confirmed anatomically coherent activations aligned with pathological regions.

Abstract

Accurate interpretation of chest radiographs is central to the early diagnosis and management of pulmonary disorders. This study introduces an explainable deep learning framework that integrates biomedical signal fidelity analysis with transfer learning to enhance diagnostic reliability and transparency. Using the publicly available COVID-19 Radiography Dataset (21,165 chest X-ray images across four classes: COVID-19, Viral Pneumonia, Lung Opacity, and Normal), three architectures, namely baseline Convolutional Neural Network (CNN), ResNet-50, and EfficientNetB3, were trained and evaluated under varied class-balancing and hyperparameter configurations. Signal preservation was quantitatively verified using the Structural Similarity Index Measure (SSIM = 0.93 ± 0.02), ensuring that preprocessing retained key diagnostic features. Among all models, ResNet-50 achieved the highest classification accuracy (93.7%) and macro-AUC = 0.97 (class-balanced), whereas EfficientNetB3 demonstrated superior generalization with reduced parameter overhead. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations confirmed anatomically coherent activations aligned with pathological lung regions, substantiating clinical interpretability. The integration of signal fidelity metrics with explainable deep learning presents a reproducible and computationally efficient framework for medical image analysis. These findings highlight the potential of signal-aware transfer learning to support reliable, transparent, and resource-efficient diagnostic decision-making in radiology and other imaging-based medical domains.

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