Classification of X-ray Images for Human Body Parts
DOI:
https://doi.org/10.61841/p2s0hk49Keywords:
Body-parts classification, handcrafted features, deep features, pre-trained CNN, joint approachAbstract
Due to advances in medical imaging technology, there is a proliferation of diagnostic images acquired in medical centres that need to be stored, analysed, retrieved and classified. The development of automatic analysis of X-ray images and classification methods is a pressing need that will have a critical impact on clinical practices by reducing human errors. Analysis of X-ray images is mostly being done by medical specialists, as it is a critical sector and people anticipate the highest level of care and service regardless of cost. Depending on just one technique to gain a high accuracy rate for every individual class is unreliable. In this paper, the classification of medical X-ray images against body parts using a pre-trained deep convolutional neural network (DCNN) and two handcrafted descriptors in a joint approach is enclosed.
Downloads
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
