Jul 2026· Asian Pacific Journal of Cancer Prevention· Vol 27 7, pp.
2525-2534
· 0 citations· 31 references
Medicine
TL;DR
These findings establish HyPerNet as a reliable, interpretable, and computationally efficient solution for clinical brain tumor classification.
Abstract
Objective
Accurate classification of brain tumors is of utmost importance in early diagnosis and treatment, which can be further enhanced through advanced deep learning techniques. This work introduces HyPerNet-a Hybrid Perception Network for brain tumor classification that integrates cutting-edge methods to maximize classification accuracy, interpretability, and efficiency. HyPerNet leverages multi-modal fusion, combining Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) scans to extract complementary information from different imaging modalities.
Methods
A dynamic attention mechanism adapts to image complexity, improving performance under noisy and artifact-laden conditions. The architecture incorporates explainable AI components using Grad-CAM for clinical interpretability, along with 3D convolutional neural networks for volumetric feature learning. Hybrid feature extraction, integrating Convolutional Neural Networks (CNNs) with graph neural networks (GNNs), enables the model to capture both local and relational tumor characteristics. Domain adaptation via transfer learning allows the model to generalize across diverse datasets, improving robustness.
RESULT
In experiments conducted on a benchmark dataset of 8,256 images, HyPerNet achieved a classification accuracy of 97.14%, precision of 97.09%, recall of 97.09%, F1-score of 97.09%, and specificity of 98.9%, outperforming state-of-the-art models such as DenseNet121, ResNet101, and MobileNetV3. Notably, HyPerNet attains these results with only 1.66 million parameters, making it highly efficient for deployment on mobile and edge devices.
Conclusion
These findings establish HyPerNet as a reliable, interpretable, and computationally efficient solution for clinical brain tumor classification.
Brain tumor (BT) classification plays a vital role in computer-aided diagnosis, as early detection directly influences treatment planning and patient survival. Advances in artificial intelligence, particularly in machine learning (ML) and deep learning (DL), have greatly improved automated tumor identification from magnetic resonance imaging (MRI), reducing reliance on manual assessment. This study proposes a hybrid framework that combines generative adversarial networks (GANs), convolutional neural networks (CNNs), and Vision Transformers (ViTs) for accurate tumor classification. GANs are used to generate realistic synthetic MRI images, addressing data scarcity and increasing dataset diversity. CNNs extract discriminative deep features, accelerating training while limiting overfitting. These features are then processed by a ViT model, capable of capturing complex spatial relationships within medical images. Experimental results show that the proposed ViT–CNN–GAN approach achieves 97.36% accuracy, outperforming conventional methods. Overall, this framework demonstrates the potential of advanced DL models to strengthen MRI-based tumor diagnosis and support more reliable clinical decision-making.
Iliass Zine-dine, J. Riffi, Khalid El Fazazy et al.· International Journal of Onl...· 0 citations
The proposed approach advances hybrid medical image analysis by introducing statistically validated, stability-oriented, and deployment-aware adaptive feature-level integration, providing a reproducible foundation for interpretable and robust neuro-diagnostic intelligence.
Jayendra S Jadhav, Sulbha Yadav, Kalyan Devappa Bamane et al.· International journal of com...· 0 citations
Brain tumor classification plays an important role in early diagnosis and treatment planning. The current study aims to evaluate and compare the performance of five pre-trained convolutional neural network (CNN) models, namely VGG16, VGG19, MobileNet, Xception, and InceptionV3 using magnetic resonance imaging (MRI) images categorized into glioma, meningioma, pituitary tumor, and no tumor classes. To enhance model performance and address class imbalance, transfer learning and data augmentation techniques were employed. To boost model interpretability, heatmaps of important areas in tumor classification were produced through gradient-weighted class activation mapping (Grad-CAM). MobileNet was the most accurate with 97% and was more precise and more sensitive. The Grad-CAM visualizations showed the models were attending to clinically relevant features, which increased the interpretability. This comparative study demonstrates the effectiveness of the integration of explainable artificial intelligence (XAI) in deep learning pipelines for reliable brain tumor diagnosis.
Md. Firoz Hasan, Md. Awal Hadi, S. Nasrin et al.· IAES International Journal o...· 0 citations
Magnetic resonance imaging is widely used for the examination of brain abnormalities because it provides detailed soft-tissue information without ionizing radiation. Nevertheless, manual interpretation of large numbers of MRI slices is time-consuming and may be affected by inter-observer variation. This paper presents a structured deep learning framework for multi-class brain tumor classification using the EfficientNet family of convolutional neural networks. The framework emphasizes consistent preprocessing, transfer learning, class-balanced augmentation, careful validation, and clinically meaningful performance reporting. Rather than treating classification as an isolated model-training task, the proposed approach connects data quality, model calibration, error analysis, and reproducibility. The paper also explains why EfficientNet is suitable for medical image classification: its compound scaling strategy balances network depth, width, and input resolution, enabling strong feature learning with comparatively efficient computation. A complete experimental protocol is described for separating training, validation, and test data at the patient level; controlling information leakage; selecting evaluation metrics; and comparing EfficientNet variants with conventional convolutional baselines. The resulting framework can support reliable thesis-level experimentation and can later be extended with visual explanation methods such as Grad-CAM. The study concludes that model efficiency alone is insufficient; dependable brain tumor classification requires disciplined data handling, transparent reporting, and external validation.
Ajay Khatri, Sanmati Jain· International Journal of Eng...· 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
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.