ArticleSensors (Basel, Switzerland)2022
A New Deep Hybrid Boosted and Ensemble Learning-Based Brain Tumor Analysis Using MRI.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Neuroimaging and machine learning fusion for improved brain tumor diagnosis and prognosis.Scientific reports · 2026Article
- Hyperparameter Optimization of Convolutional Neural Networks for Robust Tumor Image Classification.Diagnostics (Basel, Switzerland) · 2026Article
- An Optimized Strategy for Brain Tumor Classification Using SO(3) Equivariant Graph Neural Networks with Snow Geese Algorithm in MRI Imaging.Journal of molecular neuroscience : MN · 2026Article
- Lightweight deep learning for medical imaging using MobileNetV2-based brain pathology classification with Grad-CAM interpretability.Frontiers in medicine · 2026Article
- Brain Tumor Segmentation Using U-Net With ResNet50 Encoder for Enhanced MRI Analysis.International journal of biomedical imaging · 2026Article
- Machine Learning in MRI Brain Imaging: A Review of Methods, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2025Review
- A Robust Semi-Supervised Brain Tumor MRI Classification Network for Data-Constrained Clinical Environments.Diagnostics (Basel, Switzerland) · 2025Article
- Enhanced MRI brain tumor detection using deep learning in conjunction with explainable AI SHAP based diverse and multi feature analysis.Scientific reports · 2025Article
- Role of Feature Diversity in the Performance of Hybrid Models-An Investigation of Brain Tumor Classification from Brain MRI Scans.Diagnostics (Basel, Switzerland) · 2025Article
- Brain tumor detection empowered with ensemble deep learning approaches from MRI scan images.Scientific reports · 2025Article
- Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review.Sensors (Basel, Switzerland) · 2025Review
- A deep learning approach for brain tumour classification and detection in MRI images using YOLOv7.Frontiers in oncology · 2025Article
- Machine learning driven dashboard for chronic myeloid leukemia prediction using protein sequences.PloS one · 2025Article
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- Article
- Review
- Brain Tumor MRI Classification Using a Novel Deep Residual and Regional CNN.Biomedicines · 2024Article
- Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier.Bioengineering (Basel, Switzerland) · 2024Article
- Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.Cancers · 2024Review
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7 authors.
Funding
Abstract
Brain tumor analysis is essential to the timely diagnosis and effective treatment of patients. Tumor analysis is challenging because of tumor morphology factors like size, location, texture, and heteromorphic appearance in medical images. In this regard, a novel two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs). In the first phase, a novel deep-boosted features space and ensemble classifiers (DBFS-EC) scheme is proposed to effectively detect tumor MRI images from healthy individuals. The deep-boosted feature space is achieved through customized and well-performing deep convolutional neural networks (CNNs), and consequently, fed into the ensemble of machine learning (ML) classifiers. While in the second phase, a new hybrid features fusion-based brain-tumor classification approach is proposed, comprised of both static and dynamic features with an ML classifier to categorize different tumor types. The dynamic features are extracted from the proposed brain region-edge net (BRAIN-RENet) CNN, which is able to learn the heteromorphic and inconsistent behavior of various tumors. In contrast, the static features are extracted by using a histogram of gradients (HOG) feature descriptor. The effectiveness of the proposed two-phase brain tumor analysis framework is validated on two standard benchmark datasets, which were collected from Kaggle and Figshare and contain different types of tumors, including glioma, meningioma, pituitary, and normal images. Experimental results suggest that the proposed DBFS-EC detection scheme outperforms the standard and achieved accuracy (99.56%), precision (0.9991), recall (0.9899), F1-Score (0.9945), MCC (0.9892), and AUC-PR (0.9990). The classification scheme, based on the fusion of feature spaces of proposed BRAIN-RENet and HOG, outperform state-of-the-art methods significantly in terms of recall (0.9913), precision (0.9906), accuracy (99.20%), and F1-Score (0.9909) in the CE-MRI dataset.
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