Detecting the Accuracy of Cancer Stem Cell In Brain Tumor
| dc.contributor.author | ADITYA RAJ: AKASH KUMAR BARIK: KEERTHANA BALAKRISHNAN: | |
| dc.date.accessioned | 2024-10-28T11:30:49Z | |
| dc.date.available | 2024-10-28T11:30:49Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | The goal of this research is to create an auto-mated medical image analysis and detection system for reliable brain tumor categorization using MRI datasets. The study used our unique Inception-resnet-v2 architecture to identify normal brain pictures from brain tumor images in comparison to the VGG16 CNN architecture. The spectrum of Al is debatable: as robots become more capable, occupations seen as requiring "power" are usually removed from this description, a process known as the Al effect, giving rise to the adage, "Al is whatever hasn't been done yet." For example, visual character recognition is frequently removed from artificial intelligence, even though it has become a common application. Modern machine skills widely classed as Al include successfully comprehending human language, competing at the highest level in key play organizations, driving autonomously, and intelligent routing in content delivery networks and war simulations. | |
| dc.identifier.uri | http://192.168.75.5:4000/handle/123456789/15989 | |
| dc.language.iso | en | |
| dc.publisher | NHCE | |
| dc.title | Detecting the Accuracy of Cancer Stem Cell In Brain Tumor | |
| dc.type | Learning Object |