TY  - JOUR
AU  - Ali, Aamir
AU  - Raza, Aamir
AU  - Nazir, Iqra
AU  - Mohan, Swetank
AU  - Syed, Khaleelullah
AU  - Fatima, Nikhat
AU  - Ali, Misbah
PY  - 2026
DA  - 2026/03/27
TI  - Automated Brain Tumor Analysis from MRI Using Deep Learning
JO  - Biomedical Informatics and Smart Healthcare
T2  - Biomedical Informatics and Smart Healthcare
JF  - Biomedical Informatics and Smart Healthcare
VL  - 2
IS  - 1
SP  - 62
EP  - 78
DO  - 10.62762/BISH.2026.687557
UR  - https://www.icck.org/article/abs/BISH.2026.687557
KW  - brain tumor classification
KW  - magnetic resonance imaging (MRI)
KW  - convolutional neural networks
KW  - efficientNet-B0
KW  - deep learning
KW  - computer-aided diagnosis
KW  - medical image analysis
AB  - Accurate brain tumor classification from MRI remains essential for computer-assisted diagnosis, yet manual interpretation is time-consuming and variable. This study presents an EfficientNet-B0-based convolutional neural network for multi-class classification of glioma, meningioma, pituitary tumors, and no-tumor cases. The model was trained and evaluated on a public MRI dataset of 7023 images using a strict patient-level split to ensure unbiased assessment. A fixed EfficientNet-B0 backbone with a lightweight classification head reduces overfitting while maintaining stable learning. Performance was assessed via accuracy, precision, recall, F1-score, and specificity. The model achieved class-wise (one-vs-rest) accuracies of 96.5% for glioma, 99.1% for no tumor, 95.6% for meningioma, and 97.9% for pituitary tumors, with high specificity across all classes. A controlled comparison against ResNet50 and MobileNetV2 under identical conditions shows that EfficientNet-B0 offers a balanced trade-off between predictive performance and computational cost, achieving competitive accuracy with significantly fewer parameters and faster inference than deeper architectures. This study provides a reproducible evaluation framework, patient-level validation protocol, and systematic backbone comparison to support efficient and reliable deep learning models for multi-class brain tumor classification.
SN  - 3068-5524
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Ali2026Automated,
  author = {Aamir Ali and Aamir Raza and Iqra Nazir and Swetank Mohan and Khaleelullah Syed and Nikhat Fatima and Misbah Ali},
  title = {Automated Brain Tumor Analysis from MRI Using Deep Learning},
  journal = {Biomedical Informatics and Smart Healthcare},
  year = {2026},
  volume = {2},
  number = {1},
  pages = {62-78},
  doi = {10.62762/BISH.2026.687557},
  url = {https://www.icck.org/article/abs/BISH.2026.687557},
  abstract = {Accurate brain tumor classification from MRI remains essential for computer-assisted diagnosis, yet manual interpretation is time-consuming and variable. This study presents an EfficientNet-B0-based convolutional neural network for multi-class classification of glioma, meningioma, pituitary tumors, and no-tumor cases. The model was trained and evaluated on a public MRI dataset of 7023 images using a strict patient-level split to ensure unbiased assessment. A fixed EfficientNet-B0 backbone with a lightweight classification head reduces overfitting while maintaining stable learning. Performance was assessed via accuracy, precision, recall, F1-score, and specificity. The model achieved class-wise (one-vs-rest) accuracies of 96.5\% for glioma, 99.1\% for no tumor, 95.6\% for meningioma, and 97.9\% for pituitary tumors, with high specificity across all classes. A controlled comparison against ResNet50 and MobileNetV2 under identical conditions shows that EfficientNet-B0 offers a balanced trade-off between predictive performance and computational cost, achieving competitive accuracy with significantly fewer parameters and faster inference than deeper architectures. This study provides a reproducible evaluation framework, patient-level validation protocol, and systematic backbone comparison to support efficient and reliable deep learning models for multi-class brain tumor classification.},
  keywords = {brain tumor classification, magnetic resonance imaging (MRI), convolutional neural networks, efficientNet-B0, deep learning, computer-aided diagnosis, medical image analysis},
  issn = {3068-5524},
  publisher = {Institute of Central Computation and Knowledge}
}