A Modified VGG19-Based Framework for Accurate and Interpretable Real-Time Bone Fracture Detection

2025-07-31

AI Summary

To address the scarcity of radiological expertise, time-consuming and error-prone manual interpretation, and poor interpretability of existing deep learning models in X-ray fracture diagnosis, this study proposes an end-to-end interpretable fracture detection framework. Methodologically, we enhance the VGG19 architecture by integrating CLAHE-based contrast enhancement, Otsu thresholding, and Canny edge detection to improve feature robustness; additionally, we incorporate Grad-CAM to generate clinically comprehensible decision heatmaps. The system is deployed as a real-time web application with inference latency under 0.5 seconds. Evaluated on a public dataset, our model achieves 99.78% classification accuracy and an AUC of 1.00—significantly outperforming baseline methods. The core contribution lies in a practical, deployable interpretable AI solution that simultaneously ensures high diagnostic accuracy, low latency, and clinical trustworthiness, thereby offering a reliable decision-support tool for primary healthcare settings.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Early and accurate detection of the bone fracture is paramount to initiating treatment as early as possible and avoiding any delay in patient treatment and outcomes. Interpretation of X-ray image is a time consuming and error prone task, especially when resources for such interpretation are limited by lack of radiology expertise. Additionally, deep learning approaches used currently, typically suffer from misclassifications and lack interpretable explanations to clinical use. In order to overcome these challenges, we propose an automated framework of bone fracture detection using a VGG-19 model modified to our needs. It incorporates sophisticated preprocessing techniques that include Contrast Limited Adaptive Histogram Equalization (CLAHE), Otsu's thresholding, and Canny edge detection, among others, to enhance image clarity as well as to facilitate the feature extraction. Therefore, we use Grad-CAM, an Explainable AI method that can generate visual heatmaps of the model's decision making process, as a type of model interpretability, for clinicians to understand the model's decision making process. It encourages trust and helps in further clinical validation. It is deployed in a real time web application, where healthcare professionals can upload X-ray images and get the diagnostic feedback within 0.5 seconds. The performance of our modified VGG-19 model attains 99.78% classification accuracy and AUC score of 1.00, making it exceptionally good. The framework provides a reliable, fast, and interpretable solution for bone fracture detection that reasons more efficiently for diagnoses and better patient care.

Problem

• Accurate real-time bone fracture detection using modified VGG19
• Interpretable AI for clinical trust via Grad-CAM heatmaps
• Overcoming limited radiology expertise with automated X-ray analysis

Innovation

• Modified VGG19 model for fracture detection
• CLAHE and Canny edge preprocessing techniques
• Grad-CAM for interpretable AI heatmaps