الوصف

Early detection and classification of brain tumors are vital, given their potential to be life-threatening. Nevertheless, the wide range of histological variations makes it challenging for artificial intelligence models to generalize across different tumor types. Recent breakthroughs in artificial intelligence have successfully utilized various deep learning approaches to accurately detect and categorize brain malignancies. Hence, these approaches must be precise and effectively interpret their findings, as misdiagnosis can result in inappropriate treatment and prolonged recovery. Therefore, we introduce an explainable hybrid convolutional neural network that utilizes transfer learning using DenseNet121 and EfficientNetB0. This model does not rely on features extracted from a standalone network, thereby enhancing its ability to classify various types of brain tumors. In addition, a new classifier was used, comprising fully connected layers and a SoftMax layer. The approach was evaluated using two publicly available datasets. The first dataset has three classes of brain tumors: meningioma, glioma, and pituitary. On the other hand, the second one has two classes: non-tumor and tumor scans. The model achieved 99.13% and 99.56% accuracy, 98.94% and 99.56% precision, 98.98% and 99.56% recall, and 98.96% and 99.56% F1-score on three-class and two-class datasets, respectively. The proposed approach generally outperformed prior research in classifying brain tumors on MRI scans, and the implemented explainable model increased confidence.

URL

https://www.sciencedirect.com/science/article/pii/S2667305326000451

DOI

https://doi.org/10.1016/j.iswa.2026.200670

الملخص

الكلمات الدالة

;Explainable ai; Hybrid; Deep learning; Brain tumor; Transfer learning; MRI; CNN