S$$\vphantom{0}^{2}$$A-RConvNet: standalone self-attention enabled deep learning model for brain tumor classification with MRI images
A-RConvNet model attained the values of sensitivity of 97.61%, precision of 98.71%, F1-Score of 98.16%, specificity of 98.43% and accuracy of 97.98% with 90% of training using the BraTS 2021 dataset.
Authors
- Jyoti Deone (ORCID: https://orcid.org/0000-0003-4102-5645)
- Surendra Solanki (ORCID: https://orcid.org/0000-0002-5067-7621)
- Somdotta Roy Choudhury (ORCID: https://orcid.org/0000-0001-9401-3387)
- Uttam Waghmode
- Prof. Dhanashri Dhawale
- Digambar Puri
- Ashwini Naik
Institutions
- University of Mumbai (IN)
- Lokmanya Tilak Municipal General Hospital and Lokmanya Tilak Municipal Medical College (IN)
- Aditya Birla (India) (IN)
- University of Rajasthan (IN)
Publication Details
- Journal
- Scientific Reports
- Published
- 2026-04-25
- DOI
- https://doi.org/10.1038/s41598-026-46010-1
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.00