Jul 2026· International Conference Computing Methodologies and Communication· pp. 1382-1387· 0 citations· 19 references
Abstract
Magnetic resonance imaging (MRI) is very important for clinical diagnosis and treatment planning, because it can accurately detect and classify brain tumors. Traditional Convolutional Neural Network (CNN) models can extract local features, but they are not able to get long-range contextual information from complex medical images. The proposed system is to develop a CNN-Transformer based framework for automatic brain tumor detection and multi-class classification. The model combines Channel Attention Networks (CAN) with Vision Transformer based models (ViT)/Dual Vision Transformer(DaViT). Due to which the CAN enhances feature learning by spotlighting tumor-related channels. Also the model is validated on BraTS MRI dataset with four classes, i.e. pituitary tumor, meningioma, glioma and normal cases. The results obtained based on CNN-Transformer gives a higher accuracy of 97.6% and higher precision of 0.96 that enables the transformer module to capture global spatial relationships within the image.
Results show that ViT–BiLSTM's classification performance is superior to those of traditional deep learning methods: among all the tumor categories its accuracy is higher, its fine-tuning more perfect, as well as, its Recall rates greater.
Nagham Salim Mohammed, Omar S. Almolaa, A. S. Abdullah et al.· ITEGAM- Journal of Engineeri...· 0 citations
The proposed explainable deep learning framework shows great promise of helping clinical diagnosis of brain tumors to be reliable and transparent, by integrating with AI.
Mohd. Yousuf, Joy Chowdhury, Susmoy Chowdhury et al.· American Journal of Applied...· 0 citations
The proposed XAIViT framework has strong potential as an Explainable Artificial Intelligence (XAI)-based clinical decision support system for MRI-based brain tumor analysis and Gradient-weighted Class Activation Mapping-based visual explanations demonstrated that the model consistently focused on anatomically relevant...
I. H. A. Wahab, M. Jamil, Rosihan Rosihan· Engineering, Technology &...· 0 citations
The proposed model such as MM-EffiFormer demonstrated the significant classification performance by achieving Accuracy of 0.990, Sensitivity of 0.987, Specificity of 0.995, Dice Similarity Coefficient (DSC) of 0.991, outperforms the existing model FCM-SVM.
Lovenish Sharma, S. Nanda· International journal of com...· 0 citations
A novel hybrid convolutional neural networks and transformer architecture, HybCT-Net, augmented with a multi-level attention module and a regional explainability pipeline for brain tumor detection and classification is proposed, demonstrating superior performance than contemporary CNN, transformer and hybrid baselines.
Phanideep Karnati, Sukanya Roy, Dundi Urlamma et al.· International journal of com...· 0 citations
