Enhanced Level Brain Tumor Identification Using CNN, VGG16 and ResNet Models
- Title
- Enhanced Level Brain Tumor Identification Using CNN, VGG16 and ResNet Models
- Creator
- Sambandam R.K.; Jyoshna M.
- Description
- The comprehension of brain growths is significantly improved through the identification and categorization of these disorders. Still, their discovery is relatively grueling due to their variability in terms of position, shape, and size. Fortunately, deep literacy has revolutionized the field and significantly improved recognition, prediction, and opinion in various healthcare areas, including brain excrescences. The main goal of this study is to thoroughly review exploration that utilizes CNN, VGG16, and RESNET infrastructures to classify brain excrescences using MRI images. The performance of these models varied significantly, with CNN, VGG16, and RESNET achieving an emotional delicacy of 99.6. Additionally, ResNet and VGG16 achieved rigor of 92.4 and 89.7 independently. Likewise, the visualization of the decision-making processes of these models has provided valuable insight into the features they prioritize. By incorporating these models into their practice, healthcare professionals have the opportunity to enhance their individual capabilities, eventually leading to improved patient outcomes. 2024 IEEE.
- Source
- Proceedings of InC4 2024 - 2024 IEEE International Conference on Contemporary Computing and Communications
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Brain Tumor; CNN; Deep Learning; MRI Classification; RESNET; VGG16
- Coverage
- Sambandam R.K., Christ University, Dept. of Computer Science and Engineering, Bengaluru, India; Jyoshna M., Christ University, Dept. of Computer Science and Engineering, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835038365-2
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Sambandam R.K.; Jyoshna M., “Enhanced Level Brain Tumor Identification Using CNN, VGG16 and ResNet Models,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19260.