ItemBook Chapter

CNN-based approach for brain tumor detection and severity prediction

Title
CNN-based approach for brain tumor detection and severity prediction
Creator
Jenefa, J.; Athulya, R.; Varghese, Aaron John; Vetriveeran, Divya; Sambandam, Rakoth Kandan
Description
Artificial intelligence is widely used in healthcare, especially in medical imaging. It leads to advanced diagnosis using innovative approaches to analyze complex data more accurately and provide personalized treatments. This helps the clinicians efficiently analyze the imaging data, leading to early detection of diseases like brain tumors, cancer, cardiovascular diseases, etc. The research work focuses on the detection and severity prediction of brain tumors. Magnetic resonance imaging (MRI) scan images are preprocessed in the proposed model using different methods. The convolutional neural network model (CNN) is used to detect and predict brain tumors and can be used in personalized treatments. The proposed method has an accuracy of about 98% in classification and severity prediction. 2025, IGI Global Scientific Publishing. All rights reserved.
Source
Future Innovations in the Convergence of AI and Internet of Things in Medicine;pp.111-129
Date
01-01-2025
Publisher
IGI Global
Coverage
Jenefa J., Christ University, India; Athulya R., Deloitte Parks Vista, India; Varghese A.J., Sabre Travel Technologies, India; Vetriveeran D., Christ University, India; Sambandam R.K., Christ University, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-836937705-5; 979-836937703-1;
Format
online
Language
English
Type
Book chapter

Citation

Jenefa, J.; Athulya, R.; Varghese, Aaron John; Vetriveeran, Divya; Sambandam, Rakoth Kandan, “CNN-based approach for brain tumor detection and severity prediction,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25016.

Output Formats