Tensor Flow Model in Medical Image Analysis - Review
Main Article Content
Abstract
Support Vector Machines, Reinforcement algorithms, artificial neural networks are some of the Machine Learning Algorithms available in Medical Analysis. By using these algorithms, much of the research has been done in analysis of liver cancer for genome classification and identification of lesions. At present, Deep learning algorithms have quickly turned into a strategy for examine CT images. This article presents one of the major deep learning techniques named tensor flow technique to investigate images in scan for the task of visualization of abnormal condition of liver tumor in the context of shape and color towards disease diagnosis. We surveyed the utilization of tensor flow for classifying images, detection of objects, and detection of lesions. In this paper, we mainly concentrated on the study and working of tensor flow in image classification. Also, a summary of the present and future scope in this area has been presented in detail.
Downloads
Metrics
Article Details
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.