Aug 2026· Journal of Visualized Experiments· Vol 234· 0 citations
Medicine
TL;DR
Results from various performance evaluation metrics indicate that the proposed fusion-fusion-based DL model (FusionNetX) can accurately detect and predict brain tumors and help health practitioners make timely decisions.
Abstract
Brain tumors are considered one of the deadliest diseases in the world, and misdiagnosing them puts patients' lives in danger and lowers the survival rate. Magnetic resonance imaging (MRI) with deep learning-based techniques, especially convolutional neural networks, is crucial for overcoming this obstacle, as it enables a more detailed examination of the brain's internal structure and offers outstanding learning and predictive capabilities. The need for accurate diagnosis of the tumor in the brain is still significant. Therefore, an enhanced feature-fusion-based DL model (FusionNetX) is proposed that leverages the strengths of two pretrained models, DenseNet121 and EfficientNetB7, using customized fine-tuning hyperparameters, including trainable and non-trainable layers. The proposed FusionNetX model was applied to five publicly available brain tumor datasets: Br35H, Figshare, Sartaj, Masoud, and Balanced Brain Tumor. Some preprocessing steps were applied to improve image quality and address overfitting through resizing and data augmentation. Using a variety of performance evaluation metrics, such as accuracy, loss, positive predicted value, negative predictive value, true positive rate, true negative rate, F1-score, false negative rate, and false positive rate, the proposed model FusionNetX was found to have an accuracy of 99.33% for Br35H, 99.13% for Figshare, 99.89% for Masoud, 99.19% for BBT-Dataset, and 98.77% for Sartaj. Additionally, the five brain tumor datasets were evaluated using late fusion and attention-based fusion to demonstrate the significance of the proposed FusionNetX model, and the results were substantially better, with gains of 0.3% to 1.9% across all datasets. Furthermore, the receiver operating characteristic curve is computed, and results from various performance evaluation metrics indicate that the proposed FusionNetX model can accurately detect and predict brain tumors and help health practitioners make timely decisions.
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
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
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
A unique explainable deep learning model that integrates Tiny-ConvNeXt and DenseNet169 using multi-stage brain tumor classification is proposed, indicating that the suggested model provides a transparent and reliable framework for brain tumor detection, with potential applications in practical clinical decision support systems.
Md Sadi Al Huda, K. Tanvir, Zubaida Akhter et al.· Artificial Intelligence and...· 0 citations
Magnetic resonance imaging (MRI) is a crucial component of the medical diagnostic and therapeutic approach for brain tumors, as early detection of anomalous tissue considerably enhances patient outcomes. MRI images may obscure significant tumor characteristics owing to noise, inadequate contrast, and erratic intensity distributions. This work examines the application of ResNet169, EfficientNetB0, and a hybrid fused model (ResNetEffi169B0) to address challenges in deep learning-based image improvement and categorization. The performance metrics included F1-score, Accuracy, Precision, Sensitivity, Entropy, SSIM, and MSE to test the models. The results show that EfficientNetB0 has the best PSNR (11.45), SSIM (0.308), and accuracy (0.601) when it comes to classification and improvement. The ResNet169 model's high sensitivity of 0.463 showed that it could dependably find tumor regions. The hybrid approach used the best features of both styles to create balanced results that made diagnoses more consistent and images clearer. The fusion method enhances the quality and structural data of magnetic resonance imaging (MRI) scan slices, leading to more precise classification and enhanced tumor visibility. This study examines the potential of hybrid deep learning models to enhance computer-aided diagnostic tools in medical imaging for improved brain tumor identification.
Balas, Subalatha.M· 2026 6th International Confe...· 0 citations
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