Aug 2026· American Journal of Applied Research and AI· 0 citations· 24 references
TL;DR
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.
Abstract
Diagnosis of brain tumor is one of the most critical tasks in medical imaging and early and accurate diagnosis is extremely important for improving the treatment planning and outcome of patients with brain tumors. Brain tumor assessment is achieved with Magnetic Resonance Imaging (MRI), which is commonly used, but manual interpretation of MRI scans can be time-consuming and subject to inter-observer variability. To overcome these difficulties, an explainable deep learning (XDL) framework is proposed that classifies brain tumors into a multiclass classification problem using magnetic resonance imaging (MRI) images. To overcome these challenges, this study proposes an explainable deep learning (XDL) framework that is capable of brain tumor classification as a multiclass classification problem from MRI images. Five state-of-the-art deep learning architectures ResNet50, VGG16, DenseNet121, Vision Transformer (ViT), and a Hybrid CNN ViT model, were tested and compared to determine which one would be the best fit for the classification task. Preprocessing and augmentation of MRI images were performed before training and evaluation the model. Metrics used to evaluate the performance included accuracy, precision, recall, F1 score, the confusion matrix, and Receiver Operating Characteristic Area Under Curve (ROC-AUC). The Hybrid CNN–ViT framework was compared and it was found that the hybrid approach, combining the advantages of CNN for feature extraction and transformer for contextual learning, led to better classification performance. Gradient-weighted Class Activation Mapping (Grad-CAM) and Local Interpretable Model-Agnostic Explanations (LIME) were used to explain the best-performing model to improve the transparency and interpretability of the model. To validate that the model attended to clinically relevant tumour regions in classification, the explainability results were obtained. The proposed framework showed high classification accuracy of 98.68% and an average of ROC-AUC score at approximately 0.999, indicating that the framework has good predictive power and high interpretability. The results indicate that 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.
The study adds a rigorous benchmarking mechanism and empirical evidence for adopting ResNet50 as a robust model for multi-class brain tumour diagnosis and highlights the power of deep residual learning for solving some of the difficulties in classifying brain MRI, such as inter-class similarity and feature heterogeneity.
Prabha Kumaresan, Xin Tian Lim· International Journal on Rob...· 0 citations
Accurate classification of brain tumors using magnetic resonance imaging (MRI) is essential to clinical diagnosis and treatment. Nevertheless, the wide diversity in a single type of disease and high similarity between the tumors in different categories pose considerable challenges for deep learning models due to the characteristics of CNNs that are mainly for local features extracted, the necessity of reducing these limitations and constraints. This paper discusses an innovative hybrid deep learning paradigm in which an image is modeled by means of a vision transformer (ViT) and a Bi-directional long-term memory network (BiLSTM), resulting in an effective brain tumor classification. The application is based on the framework of ViT, capable of modeling overall context to extract high-level distinguishing features of MRI images, and the BiLSTM successfully capturing sequential dependencies inside of the extracted feature representations. This hybrid architecture is able to be very rich in modelling the spatial and contextual relationships that come with complex medical images. 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. This study demonstrates the efficacy of transformer-based hybrid architectures for medical image analysis with the proposal that by integrating a holistic attention framework with sequential modeling, they can yield substantially better patient diagnosing outcomes. The presented model is not only a viable recommendation for computer assisted diagnostic systems, but is also likely to help with clinical decision making in healthcare fields. The experimental results indicate that the proposed framework maintains a strong balance between accuracy and recall. Specifically, the model achieved accuracy/recall values of 92.5%/92.0% for gliomas, 91.2%/90.7% for meningiomas, and 93.5%/94.0% for brain tumors, resulting in high and consistent F1 scores across all categories.
Nagham Salim Mohammed, Omar S. Almolaa, A. S. Abdullah et al.· ITEGAM- Journal of Engineeri...· 0 citations
Accurate and explainable automated brain tumor classification using magnetic resonance imaging (MRI) remains an important challenge in medical image analysis. Transfer-learning studies often report high accuracy on public MRI benchmarks, but comparisons can be difficult to interpret when architectures are trained under different protocols or evaluated without statistical and explainability analyses. Here, we present an explainable transfer-learning framework for four-class brain tumor classification (glioma, meningioma, pituitary tumor, and no-tumor) in which MobileNetV2, ResNet50, and EfficientNetB0 are compared under a common training protocol. Models were trained and evaluated on the publicly available Brain Tumor MRI Dataset containing 7,200 T1-weighted contrast-enhanced axial images (5,600 training and 1,600 testing images, balanced across the four classes). The three backbones were fine-tuned using the same two-phase training strategy and compared using accuracy, precision, recall, F1-score, one-vs-rest ROC-AUC, confusion matrices, Cohen's kappa, Wilson 95% confidence intervals, McNemar paired tests, ablation analysis, and computational-cost measures. ResNet50 achieved the highest accuracy (96.06%; 95% CI: 94.99–96.91%) and macro-F1 (0.960), with a macro-averaged ROC-AUC of 0.990 and Cohen's kappa of 0.948. Its difference from EfficientNetB0 was statistically significant (McNemar, p = 0.003), whereas the difference from MobileNetV2 was not (p = 0.263). MobileNetV2 retained 99.6% of ResNet50's accuracy with substantially lower parameter and arithmetic cost. Per-class analysis identified glioma as the most difficult class. Grad-CAM was examined on correct and incorrect predictions and cross-checked with complementary attribution methods; a quantitative border-mass analysis over 200 correctly classified images per group did not support the specific hypothesis that no-tumor predictions were driven by a top-border watermark. Overall, the study supports controlled statistical, computational, and explainability analysis as a useful framework for benchmarking brain tumor MRI classifiers, while external validation and broader robustness assessment remain necessary before clinical use.
Sif K. Ebis· Journal of Al-Farabi for Eng...· 0 citations
This study investigates the application of deep learning architectures, including Convolutional Neural Network, VGG16, VGG19, ResNet50, and MobileNet, for brain tumor detection and classification and confirms that advanced deep learning architectures not only achieve high classification accuracy but also improve interpretability, thereby offering reliable and clinically applicable solutions for automated brain tumor diagnosis.
H. Uzel, Feyyaz Alpsalaz, Yıldırım Özüpak et al.· Computers and Electronics in...· 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 tumor regions, thereby improving transparency and trustworthiness.
A brain tumor classification system integrated with Explainable Artificial Intelligence (XAI) was developed using MRI images and demonstrated effective classification performance and improved interpretability, making it suitable for automated brain tumor diagnosis.
T. H. Stephen, A. Oke, A. S. Falohun et al.· LAUTECH Journal of Engineeri...· 0 citations