Modified Image Segmentation Schemes for Detection & Identification of MRI Brain Tumor Infection
Main Article Content
Abstract
Nowadays an advancement in the medical field has an additional contribution to the human health care system. Many of the time, it cannot be possible to predict MRI brain tumor with naked eyes and unable to identify the disease stage. The patient may lost their live due un accurate and delayed diagnosis. Only CT scan and MRI cannot identify the disease, required supporting soft computing tools (PSO -particle swarm optimization) to diagnose with high accuracy. To perform accurate detection and analysis of the infected tumor, need some additional technical effort. The proposed concept suggests an algorithm using soft computing to identify infected regions of brain tumor along with its comparison with other existing image segmentation method. Result analysis shows that the result obtained with the proposed method are superior to existing segmentation techniques.
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
