Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Yadav, Swapnil; Singhal, Prateek; Singh, Madan Title A name given to the resource Deep Learning Ensemble for Neuro-Oncological Diagnostics: Fusion of VGG-16 and Convolutional Neural Network Architectures Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of the IEEE International Conference Image Information Processing;pp.162-168 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICIIP68302.2025.11346209" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICIIP68302.2025.11346209</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033212234?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033212234?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Brain Tumor; CNN; Deep Learning; Hybrid Model; MRI; VGG-16 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISSN: 2640074X; ISBN: 979-833155618-1; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online