Deep Learning Ensemble for Neuro-Oncological Diagnostics: Fusion of VGG-16 and Convolutional Neural Network Architectures
- Title
- Deep Learning Ensemble for Neuro-Oncological Diagnostics: Fusion of VGG-16 and Convolutional Neural Network Architectures
- Creator
- Yadav, Swapnil; Singhal, Prateek; Singh, Madan
- Description
- Brain tumors are potentially fatal, prompt and accurate diagnosis is essential to appropriate treatment and management. MRI is a key method for locating tumors in the brain. This study introduces a HYBRID deep learning for binary classification of brain tumors, combining a pre trained VGG16 model with tailored CNN and Neural Networks. The fusion of these models is done via feature concatenation followed by a common classifier. This fusion helps in capturing both high-level abstract and task-specific features critical for classification. To help minimize overfitting and improve generalization, the models are subjected to rigorous data augmentation including rotation, zooming, and horizontal flipping, normalization, and resizing of images to 150 150 pixels. All models are trained and validated using the same data splits. Performance is determined by accuracy, training and validation loss, confusion matrices, and visualization with Matplotlib plots and Plotly which provide a vivid insight into the models. Experiments are conducted to determine the different model performances and the hybrid model attained an accuracy of 98.14%, which was higher than the standalone VGG16 (93%), CNN (91%), and NN (88%) models. 2025 IEEE.
- Source
- Proceedings of the IEEE International Conference Image Information Processing;pp.162-168
- Date
- 01-01-2025
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Brain Tumor; CNN; Deep Learning; Hybrid Model; MRI; VGG-16
- Coverage
- Yadav S., Christ (Deemed to be University), School of Sciences, Department of Computer Science, Delhi-NCR, Ghaziabad, India; Singhal P., Christ (Deemed to be University), School of Sciences, Department of Computer Science, Delhi-NCR, Ghaziabad, India; Singh M., Christ (Deemed to be University), School of Sciences, Department of Computer Science, Delhi-NCR, Ghaziabad, India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISSN: 2640074X; ISBN: 979-833155618-1;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Yadav, Swapnil; Singhal, Prateek; Singh, Madan, “Deep Learning Ensemble for Neuro-Oncological Diagnostics: Fusion of VGG-16 and Convolutional Neural Network Architectures,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 20, 2026, https://archives.christuniversity.in/items/show/26035.