Aug 2026· Engineering, Technology & Applied Science Research· 0 citations· 38 references
TL;DR
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 tumor regions, thereby improving transparency and trustworthiness.
Abstract
Brain tumors require accurate and early diagnosis to support effective treatment decisions. Magnetic Resonance Imaging (MRI) is widely used for brain tumor assessment; however, many deep learning-based approaches remain black-box systems with limited clinical interpretability. This study proposes Explainable Artificial Intelligence Vision Transformer (XAIViT), a hybrid Convolutional Neural Network (CNN)–Transformer architecture designed to improve both classification performance and model interpretability for four-class brain tumor classification. The proposed framework integrates EfficientNet-B3 as a convolutional feature extractor with a Transformer Encoder to capture global contextual information through self-attention mechanisms. Experiments were conducted using the Brain Tumor MRI Dataset from Kaggle, which consists of 7023 MRI images across four classes: glioma, meningioma, pituitary, and no-tumor. Data preprocessing included intensity normalization, data augmentation, stratified dataset splitting, and early stopping strategies during training. The proposed XAIViT framework achieved a classification accuracy of 99.24%, with macro-average precision, recall, and F1-score values exceeding 0.99 on the test dataset. Furthermore, Gradient-weighted Class Activation Mapping (Grad-CAM)-based visual explanations demonstrated that the model consistently focused on anatomically relevant tumor regions, thereby improving transparency and trustworthiness. These findings indicate that the proposed XAIViT framework has strong potential as an Explainable Artificial Intelligence (XAI)-based clinical decision support system for MRI-based brain tumor analysis.
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 objective of this research is to develop a lightweight yet accurate deep learning framework for classifying brain tumors into four categories-gliomas, meningiomas, pituitary tumors, and healthy brain tissue-using magnetic resonance imaging (MRI) data. The proposed methodology combines transfer learning with three pre-trained convolutional neural network models-MobileNetV2, EfficientNetV1, and Inception-ResNet-V2-integrated with an attention mechanism that emulates the human visual system's ability to focus on clinically relevant regions of an image. This attention-guided approach enhances tumor localization while suppressing irrelevant background information. The framework is evaluated on a large, publicly available brain MRI dataset containing thousands of labeled images across the four classes. Standard preprocessing, data augmentation, and training protocols are employed to allow straightforward replication of the proposed method. Experimental results demonstrate that EfficientNetV1 achieves the highest classification performance, reaching superior accuracy compared to both MobileNetV2 and Inception-ResNet-V2. The study concludes that the proposed lightweight, attention-based framework effectively balances accuracy and computational complexity, making it highly suitable for real-time and mobile healthcare applications, particularly in resource-constrained environments.
Gokapay Dilip Kumar, V. S. Rao, Sudheer Reddy Bandi et al.· Journal of Visualized Experi...· 0 citations
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.
Chandana T N, R. Naidu, S. V· International Conference Com...· 0 citations
It is well known fact that in medical area, identifying the brain tumors properly through Magnetic Resonance Imaging (MRI) scans becomes essential for curing them within early time-frame and planning effectively for their treatments. While convolutional neural networks (CNNs) are popular in their work for finding local details in images, but, till now, they often have shortcomings on capturing broader context across the whole scan. New technology of Transformers, like the Swin model, can help eradicating these problems in understanding these global relationships but it also has some performance problem, when we use it standalone. To get rid of both issues, our study proposes SXM-Net, which is a hybrid deep ensemble model that utilizes a Swin Transformer model with CNN models especially Xception and MobileNetV2. Our approach setup classifies brain tumors into four types: glioma, meningioma, pituitary tumors, and normal (healthy brain with no tumor) tissue. Our proposed ensemble model expresses the superior experimental performance outcome in two different aspects: first one as compared to individual convolutional network model like Xception and mobileNetV2 seperately, and second one when we have hybrid convolutional networks like hybrid of Xception and mobileNetV2. But if we compare our ensemble model with individual swin transformer, then also our proposed model gives better result. Experiments were conducted on a dataset containing MRI images with multi-class tumors. The proposed ensemble SXM-Net for popular multi- categories of brain tumors achieved better test accuracy, with high average sensitivity values and high average specificity values, giving better experimental specific outcomes than individual traditional models.
Asif Moiz, Bambam Kumar, Konduru Sravan Kumar et al.· 2026 International Conferenc...· 0 citations
Automated and accurate classification of brain tumors from MRI (magnetic resonance imaging) for clinical applications is essential. However, it still poses a challenge owing to multiple factors. This includes the high intra-class heterogeneity, vague tumor boundaries, and lack of transparency in diagnostic reasoning. In this paper, we propose 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. The framework employs local feature extraction of deep CNN encoders and the long-range dependency modeling capacity of lightweight vision transformers in conjunction. The MLAM incorporates attention mechanisms such as channel-wise squeeze-and-excitation gating, spatial convolutional block attention, and patch-based multi-head self-attention to enhance salient features at multiple semantic scales. A Hybrid Feature Fusion (HFF) is a block for adaptive fusion of the CNN feature maps and the transformer tokens that bridges the semantic gap between the convolution-based and attention-based features [24]. Moreover, the REP combines Gradient-weighted Class Activation Mapping with transformer attention rollout maps for producing regional heatmaps at pixel-wise spatial detail, enhancing clinical trust. The efficacy of the proposed model is established through extensive experiments on a multi-class brain MRI dataset with glioma, meningioma, pituitary tumor and no tumor. HybCT-Net attains a classification accuracy of 98.78%, with a macro-F1 score of 98.70% and an AUC of 0.9943. Comparative experiments demonstrate superior performance than contemporary CNN, transformer and hybrid baselines. The contributions of various architectural components are validated using ablation studies and computational analysis shows deployment feasibility. Qualitative visualizations suggest that the regional explainability maps correlate well with boundaries of the tumor as annotated by radiologists. Thus, the framework has the potential for use in clinical decision support in the real world.
Phanideep Karnati, Sukanya Roy, Dundi Urlamma et al.· International journal of com...· 0 citations
Brain tumors are becoming life-threatening issues when not treated at earlier stages. Accurate brain tumor detection is essential in order to support clinical decisions and diagnosis. The study uses the multimodal deep learning framework for brain tumor classification by effectively combining the EfficientNet-B7 network and vision transformer architectures. Modality specific feature extraction has been carried out using the EfficientNet-B7 network for both the Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Then feature maps are converted into token sequence and passed through the transformer encoder layers in order to effectively model the global dependencies across the image. Then these features are fused and then classified using the fully connected layers whether it is healthy or tumor. Based on the experimental evaluation, the proposed model in this study 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, Saroj Kumar Nanda· International journal of com...· 0 citations