Effective diagnosis of brain tumor classification by using novel DCNN
Abstract
Brain tumors are the most grave and intricate diseases to be diagnosed and treated. In this study, a novel DCNN architecture is proposed. It has an end-to-end learning framework that automatically detects and refines relevant features directly from MRI images. The model will be trained and evaluated on a large-scale brain tumor MRI dataset that comprises SARTAJ, FIGSHARE, and BraTS datasets, which are the most widely used US brain tumor MRI datasets. In order to provide a fair and accurate performance evaluation, several transfer learning models and the concatenation of models will be utilized. The proposed DCNN outperforms pretrained models.
Fields of Study & Subjects
Brain Tumor Detection and Classification
Advanced Neural Network Applications
Glioma Diagnosis and Treatment
Publication details
- Journal / source
- Computer Science Journal of Moldova
- Volume
- 33
- Issue
- 3
- Pages
- 302–321
- Publication date
- 1 January 2026
- Language
- EN