Pre-trained Under Noise: A Framework for Robust Bone Fracture Detection in Medical Imaging

TL;DR

This study evaluates the robustness of pre-trained deep learning models for bone fracture detection in noisy X-ray images, highlighting their performance degradation under varying image quality conditions to improve medical imaging diagnostics.

Contribution

It introduces a methodological framework for assessing AI model robustness against noise in medical imaging using transfer learning and controlled noise augmentation.

Findings

ResNet50 outperforms other models under noisy conditions

Noise significantly impacts fracture detection accuracy

Framework helps evaluate model degradation in real-world scenarios

Abstract

Medical Imagings are considered one of the crucial diagnostic tools for different bones-related diseases, especially bones fractures. This paper investigates the robustness of pre-trained deep learning models for classifying bone fractures in X-ray images and seeks to address global healthcare disparity through the lens of technology. Three deep learning models have been tested under varying simulated equipment quality conditions. ResNet50, VGG16 and EfficientNetv2 are the three pre-trained architectures which are compared. These models were used to perform bone fracture classification as images were progressively degraded using noise. This paper specifically empirically studies how the noise can affect the bone fractures detection and how the pre-trained models performance can be changes due to the noise that affect the quality of the X-ray images. This paper aims to help replicate real world challenges experienced by medical imaging technicians across the world. Thus, this paper establishes a methodological framework for assessing AI model degradation using transfer learning and controlled noise augmentation. The findings provide practical insight into how robust and generalizable different pre-trained deep learning powered computer vision models can be when used in different contexts.