ArticleDiagnostics (Basel, Switzerland)2020
Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 1 of them a synthesis that pooled it.
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Who cites it
77 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of the hybrid machine learning models for brain tumour segmentation and detection in medical images.Frontiers in artificial intelligence · 2025Pooled it
- Advanced Deep Learning Architectures in MRI-Based Brain Tumor Classification: A Systematic Review Focused on Meningiomas.Journal of imaging informatics in medicine · 2026Review
- An explainable multi-stage framework for brain tumor classification using hybrid feature fusion and EfficientNetB5 model.Scientific reports · 2026Article
- Evaluating the detection of small brain lesions in magnetic resonance using deep learning.Frontiers in neuroscience · 2026Article
- MambaGLA-NPC: a 3D framework integrating CNN and state space models for robust multi-center nasopharyngeal carcinoma segmentation.Frontiers in genome editing · 2026Article
- XcepFusion for brain tumor detection using a hybrid transfer learning framework with layer pruning and freezing.Scientific reports · 2025Article
- Federated Learning Framework for Brain Tumor Detection Using MRI Images in Non-IID Data Distributions.Journal of imaging informatics in medicine · 2025Article
- Multi-Class Brain Tumor Grades Classification Using a Deep Learning-Based Majority Voting Algorithm and Its Validation Using Explainable-AI.Journal of imaging informatics in medicine · 2025Article
- IV3TM: Inception V3 enabled bidirectional long short-term memory network for brain tumor classification.PloS one · 2025Article
- Explainable AI-driven MRI-based brain tumor classification: a novel deep learning approach.Frontiers in artificial intelligence · 2025Article
- MediScan: A Framework of U-Health and Prognostic AI Assessment on Medical Imaging.Journal of imaging · 2024Article
- Prediction of early recurrence of adult-type diffuse gliomas following radiotherapy using multi-modal magnetic resonance images.Medical physics · 2024Article
- Enhancing brain tumor classification through ensemble attention mechanism.Scientific reports · 2024Article
- A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification.Diagnostics (Basel, Switzerland) · 2024Article
- Brain Tumor MRI Classification Using a Novel Deep Residual and Regional CNN.Biomedicines · 2024Article
- Hybrid representation learning for cognitive diagnosis in late-life depression over 5 years with structural MRI.Medical image analysis · 2024Article
- A hybridized feature extraction for COVID-19 multi-class classification on computed tomography images.Heliyon · 2024Article
- Performance of convolutional neural networks for the classification of brain tumors using magnetic resonance imaging.Heliyon · 2024Article
- A novel approach to brain tumor detection using K-Means++, SGLDM, ResNet50, and synthetic data augmentation.Frontiers in physiology · 2024Article
- A deep learning model for the localization and extraction of brain tumors from MR images using YOLOv7 and grab cut algorithm.Frontiers in oncology · 2024Article
17 more citing papers are in PubMed but not listed here.
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7 authors.
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Abstract
Manual identification of brain tumors is an error-prone and tedious process for radiologists; therefore, it is crucial to adopt an automated system. The binary classification process, such as malignant or benign is relatively trivial; whereas, the multimodal brain tumors classification (T1, T2, T1CE, and Flair) is a challenging task for radiologists. Here, we present an automated multimodal classification method using deep learning for brain tumor type classification. The proposed method consists of five core steps. In the first step, the linear contrast stretching is employed using edge-based histogram equalization and discrete cosine transform (DCT). In the second step, deep learning feature extraction is performed. By utilizing transfer learning, two pre-trained convolutional neural network (CNN) models, namely VGG16 and VGG19, were used for feature extraction. In the third step, a correntropy-based joint learning approach was implemented along with the extreme learning machine (ELM) for the selection of best features. In the fourth step, the partial least square (PLS)-based robust covariant features were fused in one matrix. The combined matrix was fed to ELM for final classification. The proposed method was validated on the BraTS datasets and an accuracy of 97.8%, 96.9%, 92.5% for BraTs2015, BraTs2017, and BraTs2018, respectively, was achieved.
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