Version 1
: Received: 29 November 2023 / Approved: 30 November 2023 / Online: 30 November 2023 (14:10:07 CET)
How to cite:
Naeem, S.; Naseer, A.; Rehman, S.U.; Gruhn, V.; Akram, S. Enhancing Finger Fracture Diagnosis: A Deep Learning Approach Using ResNet and VGG. Preprints2023, 2023111990. https://doi.org/10.20944/preprints202311.1990.v1
Naeem, S.; Naseer, A.; Rehman, S.U.; Gruhn, V.; Akram, S. Enhancing Finger Fracture Diagnosis: A Deep Learning Approach Using ResNet and VGG. Preprints 2023, 2023111990. https://doi.org/10.20944/preprints202311.1990.v1
Naeem, S.; Naseer, A.; Rehman, S.U.; Gruhn, V.; Akram, S. Enhancing Finger Fracture Diagnosis: A Deep Learning Approach Using ResNet and VGG. Preprints2023, 2023111990. https://doi.org/10.20944/preprints202311.1990.v1
APA Style
Naeem, S., Naseer, A., Rehman, S.U., Gruhn, V., & Akram, S. (2023). Enhancing Finger Fracture Diagnosis: A Deep Learning Approach Using ResNet and VGG. Preprints. https://doi.org/10.20944/preprints202311.1990.v1
Chicago/Turabian Style
Naeem, S., Volker Gruhn and Sheeraz Akram. 2023 "Enhancing Finger Fracture Diagnosis: A Deep Learning Approach Using ResNet and VGG" Preprints. https://doi.org/10.20944/preprints202311.1990.v1
Abstract
Our daily activities hinge on the flexibility of our fingers, and a fractured finger can significantly disrupt these routines. The finger bones enable us to bend and fold the fingers and thumb to pick up or grasp objects and do all of our daily activities. A broken finger can cause adverse effects on our daily life activities. It is important to treat broken finger as soon as possible. Swift and precise treatment begins with capturing multiple X-rays, followed by the critical step of fracture detection in these images. Relying on the naked eye for this task carries the risk of overlooking small fractures. To address this issue an automated diagnoses of fractured fingers from images is required for which the current research employs advanced deep learning models—ResNet34, ResNet50, ResNet101, ResNet152, VGG-16, and VGG-19—to classify finger images from the Musculoskeletal Radiographs (MURA) dataset as either fractured or non-fractured. The results emphasize the consistently strong performance of ResNet models, attaining an impressive accuracy of 81.9%. This surpasses VGG models by 3.4% and establishes ResNet as the new benchmark for state-of-the-art accuracy.
Keywords
Classification; Transfer Learning; Deep learning; Finger Fracture; X-Rays
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
