Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Classification of Human Bones Using Deep Convolutional Neural Network
13
Zitationen
3
Autoren
2019
Jahr
Abstract
In human body, there are total 206 types of different bones. Each bone has its own importance. It is very important to correctly identify human bone and then suggest treatment. To classify the human bones, we will use Musculoskeletal Radiographs (MURA) dataset. MURA dataset is one of the largest public radiographic image datasets. MURA dataset contains total 40,005 x-ray images of 14,052 patients, in which 36,808 images use as a training set and rest 3197 images use the testing set. These all images belong to seven different categories of bones such as finger, elbow, hand, forearm, humerus, wrist and shoulder. This paper aims to present a novel classification method using a deep convolutional neural network (DCNN). Dataset is freely available at https://stanfordmlgroup.github.io/competitions/mura.
Ähnliche Arbeiten
A survey on deep learning in medical image analysis
2017 · 13.514 Zit.
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
2020 · 7.637 Zit.
Calculation of average PSNR differences between RD-curves
2001 · 4.088 Zit.
Magnetic Resonance Classification of Lumbar Intervertebral Disc Degeneration
2001 · 3.882 Zit.
Vertebral fracture assessment using a semiquantitative technique
1993 · 3.601 Zit.