Brain Tumor Prediction Using CNN Architecture and Augmentation Techniques: Analytical Results
- Title
- Brain Tumor Prediction Using CNN Architecture and Augmentation Techniques: Analytical Results
- Creator
- Mishra S.; Elappila M.; Yogish D.
- Description
- The brain, a complex organ central to human functioning, is susceptible to the development of abnormal cell growth leading to a condition known as brain cancer. This devastating disease poses unique challenges due to the intricate nature of brain tissue, making accurate and timely diagnosis critical for effective treatment. This research explores automated brain tumor prediction through Convolutional Neural Networks (CNNs) and augmentation techniques. Utilizing a task reused learning approach with the help of VGG-16, Mobile-Net and Xception architecture, the proposed model achieves exceptional accuracy (99.54%, 99.72%) and robust metrics. This Research explores the Augmentation techniques to enhance the precision and accuracy of the model used. The study surveys related models, emphasizing advancements in automated brain tumor classification. Results demonstrate the efficacy of the model, showcasing its potential for real-world applications in medical image analysis. Future directions involve dataset expansion, alternative architectures, and incorporating explanation techniques. This research contributes to the evolving landscape of artificial intelligence in healthcare, offering a promising avenue for accurate and efficient brain tumor diagnosis. 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
- Accuracy; Automated Brain Tumor Classification; Brain Tumor Prediction; Classification; CNN; F1 Score; Machine Learning; MobileNet; Supervised Learning Algorithms; VGG-16; Xception
- Coverage
- Mishra S., CHRIST(Deemed to be University), Department of Computer Science and Engineering, Bangalore, India; Elappila M., CHRIST(Deemed to be University), Department of Computer Science and Engineering, Bangalore, India; Yogish D., CHRIST(Deemed to be University), Department of Computer Science and Engineering, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835038365-2
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Mishra S.; Elappila M.; Yogish D., “Brain Tumor Prediction Using CNN Architecture and Augmentation Techniques: Analytical Results,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 26, 2025, https://archives.christuniversity.in/items/show/19270.