ArticleScientific reports2025
Dilated SE-DenseNet for brain tumor MRI classification.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Multi-class classification of brain tumor using a ResNet101 backbone integrated with multi-scale deformable attention module and advanced data augmentations.Scientific reports · 2026Article
- CTRNet: a lightweight and efficient deep learning model for field maize whorl identification.Scientific reports · 2026Article
- High-accuracy brain tumor detection method based on deep learning.Scientific reports · 2026Article
- Toward reliable computer-aided brain tumor diagnosis: a contrast-enhanced deep learning approach with hybrid KNN classification.Frontiers in human neuroscience · 2026Article
- Convolutional neural networks in brain disease diagnosis: a unified review of Alzheimer's, Parkinson's, and brain tumor classification.Frontiers in neuroscience · 2026Review
- Deep Learning for Brain Tumour Analysis: A Systematic Review of CNN-Transformer Hybrids in Multimodal Imaging.International journal of biomedical imaging · 2026Review
- Feature fusion context attention gate UNet for detection of polycystic ovary syndrome.Scientific reports · 2025Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Majority Voting Ensemble of Deep CNNs for Robust MRI-Based Brain Tumor Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Automated multi-class MRI brain tumor classification and segmentation using deformable attention and saliency mapping.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
In the field of medical imaging, particularly MRI-based brain tumor classification, we propose an advanced convolutional neural network (CNN) leveraging the DenseNet-121 architecture, enhanced with dilated convolutional layers and Squeeze-and-Excitation (SE) networks' attention mechanisms. This novel approach aims to improve upon state-of-the-art methods of tumor identification. Our model, trained and evaluated on a comprehensive Kaggle brain tumor dataset, demonstrated superior performance over established convolution-based and transformer-based models: ResNet-101, VGG-19, original DenseNet-121, MobileNet-V2, ViT-L/16, and Swin-B across key metrics: F1-score, accuracy, precision, and recall. The results underscore the effectiveness of our architectural enhancements in medical image analysis. Future research directions include optimizing dilation layers and exploring various architectural configurations. The study highlights the significant role of machine learning in improving diagnostic accuracy in medical imaging, with potential applications extending beyond brain tumor detection to other medical imaging tasks.
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Registered trials
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