ArticleScientific reports2025
Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images.
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 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Responsible artificial intelligence in medical imaging: a systematic review.Frontiers in digital health · 2026Pooled it
- Multimodal imaging and AI in brain tumor surgery: current tools and emerging integration.npj biomedical innovations · 2026Review
- Integrating MobileNetV3 and SqueezeNet for Multi-class Brain Tumor Classification.Journal of imaging informatics in medicine · 2026Article
- A comprehensive review of explainable artificial intelligence in healthcare methods, evaluation, and clinical integration.iScience · 2026Review
- A Robust ConvNeXt-Based Framework for Efficient, Generalizable, and Explainable Brain Tumor Classification on MRI.Bioengineering (Basel, Switzerland) · 2026Article
- Explainable Hybrid Deep Learning Framework Integrating MobileNetV2, EfficientNetV2B0, and KNN for MRI-Based Brain Tumor Classification.Cellular and molecular neurobiology · 2026Article
- Hybrid 2D/3D CNN and radiomics model for brain tumor classification using EfficientNetb0 and ResNet-18.BMC medical imaging · 2026Article
- Global-local feature fusion: a robust hybrid deep learning model for multiclass brain tumor classification with Grad-CAM++ interpretation.Frontiers in genetics · 2026Article
- Brain Tumor Segmentation Using U-Net With ResNet50 Encoder for Enhanced MRI Analysis.International journal of biomedical imaging · 2026Article
- A deep learning based NeuroFusionNet approach for automated brain tumor diagnosis from MRI.Frontiers in neuroinformatics · 2026Article
- Microwave head imaging systems for early brain tumor detection: antenna designs and emerging substrates.MethodsX · 2025Review
- Multi-class brain tumor MRI segmentation and classification using deep learning and machine learning approaches.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Automated multi-class MRI brain tumor classification and segmentation using deformable attention and saliency mapping.Scientific reports · 2025Article
- Deep learning-based multimodal risk stratification for atherosclerosis management.Archives of medical science : AMS · 2025Article
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6 authors.
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Abstract
Detecting brain tumours (BT) early improves treatment possibilities and increases patient survival rates. Magnetic resonance imaging (MRI) scanning offers more comprehensive information, such as better contrast and clarity, than any alternative scanning process. Manually separating BTs from several MRI images gathered in medical practice for cancer analysis is challenging and time-consuming. Tumours and MRI scans of the brain are exposed utilizing methods and machine learning technologies, simplifying the process for doctors. MRI images can sometimes appear normal even when a patient has a tumour or malignancy. Deep learning approaches have recently depended on deep convolutional neural networks to analyze medical images with promising outcomes. It supports saving lives faster and rectifying some medical errors. With this motivation, this article presents a new explainable artificial intelligence with semantic segmentation and Bayesian machine learning for brain tumors (XAISS-BMLBT) technique. The presented XAISS-BMLBT technique mainly concentrates on the semantic segmentation and classification of BT in MRI images. The presented XAISS-BMLBT approach initially involves bilateral filtering-based image pre-processing to eliminate the noise. Next, the XAISS-BMLBT technique performs the MEDU-Net+ segmentation process to define the impacted brain regions. For the feature extraction process, the ResNet50 model is utilized. Furthermore, the Bayesian regularized artificial neural network (BRANN) model is used to identify the presence of BTs. Finally, an improved radial movement optimization model is employed for the hyperparameter tuning of the BRANN technique. To highlight the improved performance of the XAISS-BMLBT technique, a series of simulations were accomplished by utilizing a benchmark database. The experimental validation of the XAISS-BMLBT technique portrayed a superior accuracy value of 97.75% over existing models.
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