Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Recurso digital |
| Lenguaje: | inglés |
| Publicado: |
Zenodo
2026
|
| Acceso en línea: | https://doi.org/10.5281/zenodo.19916298 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866901154001059840 |
|---|---|
| author | Professor Rajendra Pawar Omkar Walunj Pranav Hole Sarthak Thigale Sohan Sandbhor |
| author_facet | Professor Rajendra Pawar Omkar Walunj Pranav Hole Sarthak Thigale Sohan Sandbhor |
| contents | Early detection of brain tumors is crucial for effective treatment and improved patient outcomes. This study presents an automated system for brain tumor classification using deep learning techniques. A convolutional neural network based on the VGG16 architecture is utilized to analyze MRI images and classify them into different categories such as glioma, meningioma, pituitary tumor, and normal cases. The system includes image preprocessing, model prediction, and a web-based interface developed using Flask for easy user interaction. Users can upload MRI images and receive instant predictions along with confidence scores. Additionally, a PDF report is generated to present the results in a structured format. The proposed approach demonstrates reliable performance and can assist medical professionals in making faster and more accurate preliminary diagnoses. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19916298 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | MRI-Based Brain Tumor Detection Using Deep Learning Professor Rajendra Pawar Omkar Walunj Pranav Hole Sarthak Thigale Sohan Sandbhor Early detection of brain tumors is crucial for effective treatment and improved patient outcomes. This study presents an automated system for brain tumor classification using deep learning techniques. A convolutional neural network based on the VGG16 architecture is utilized to analyze MRI images and classify them into different categories such as glioma, meningioma, pituitary tumor, and normal cases. The system includes image preprocessing, model prediction, and a web-based interface developed using Flask for easy user interaction. Users can upload MRI images and receive instant predictions along with confidence scores. Additionally, a PDF report is generated to present the results in a structured format. The proposed approach demonstrates reliable performance and can assist medical professionals in making faster and more accurate preliminary diagnoses. |
| title | MRI-Based Brain Tumor Detection Using Deep Learning |
| url | https://doi.org/10.5281/zenodo.19916298 |