Detection of Bone Fracture Using Deep Learning

Abstract

This research delves into the realm of bone fracture detection in medical X-ray images by harnessing the power of Deep Learning, specifically employing the DenseNet and VGG19 Convolutional Neural Network (CNN) architectures. Through extensive training and meticulous optimization, we navigate the intricacies of these models in the context of radiology, aiming to augment the precision and effectiveness of fracture identification. Our investigation unfolds against the backdrop of a comprehensive dataset comprising a diverse array of X-ray images, encompassing both normal and fractured bone instances. Rigorous experimentation and evaluation, utilizing a spectrum of performance measures encompassing accuracy, precision and recall reveal compelling results. Our CNN models exhibit a superior diagnostic prowess, surpassing conventional methodologies and demonstrating remarkable sensitivity and specificity in fracture detection. Beyond these empirical achievements, we explore the clinical implications of our findings, envisioning these models as indispensable tools that could empower radiologists to expedite accurate diagnoses, ultimately elevating the standard of patient care and alleviating the burdens on healthcare practitioners. In sum, this study sets the stage for transformative advancements within the field of analysis of medical images and computer-aided diagnosis, ushering in a new eraof fracture detection within the realm of radiological sciences.

Ähnliche Arbeiten