Hybrid Deep Learning for Brain Tumor Detection: Combining DenseNet and Custom CNN for Enhanced Accuracy
Incorporating DenseNet with a custom CNN model can increase the capabilities of brain tumor detection in medical imaging. This hybrid performance can use both general-purpose deep learning and domain-specific feature engineering, and it provides a practical suggestion of the approach that involves a satisfactory solution in the diagnostic sense. It verifies that the combination of more than 2 methods is productive in enhancing the results of medical image classification activities.
Authors
- Almas Begum (ORCID: https://orcid.org/0000-0002-0955-7185)
- R. Nadarajan (ORCID: https://orcid.org/0000-0002-9398-2255)
- Praveen Kumar Balachandran (ORCID: https://orcid.org/0000-0003-1561-1376)
- K. Ramamoorthy (ORCID: https://orcid.org/0000-0002-7200-8007)
- Sekar Kannan
- A. Swaminathan (ORCID: https://orcid.org/0009-0004-3223-4538)
- S. Krishnamoorthy
Institutions
- Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (IN)
- Deccan College of Medical Sciences (IN)
- Varsha Bioscience and Technology (India) (IN)
- Hindustan Institute of Technology and Science (IN)
- Kebri Dehar University
- Chitkara University (IN)
- Saveetha University (IN)
Publication Details
- Journal
- Biomedical Engineering and Computational Biology
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1177/11795972251395954
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.00