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.

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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
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article

S$$\vphantom{0}^{2}$$A-RConvNet: standalone self-attention enabled deep learning model for brain tumor classification with MRI images

Jyoti Deone, Surendra Solanki, Somdotta Roy Choudhury, Uttam Waghmode et al.
Scientific Reports
Brain Tumor Detection and Classification
article

S$$\vphantom{0}^{2}$$A-RConvNet: standalone self-attention enabled deep learning model for brain tumor classification with MRI images

Jyoti Deone, Surendra Solanki, Somdotta Roy Choudhury, Uttam Waghmode, Prof. Dhanashri Dhawale, Digambar Puri, Ashwini Naik
article en

Abstract

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.

Scientific Reports
University of Mumbai (IN), Lokmanya Tilak Municipal General Hospital and Lokmanya Tilak Municipal Medical College (IN), Aditya Birla (India) (IN), University of Rajasthan (IN)
Good health and well-being
Openalex Percentile: Top 10%
Brain Tumor Detection and Classification
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