ArticleBrain informatics2018
Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network.
Article in Brain informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
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- Hybrid CNN-GCN framework for brain tumor MRI classification: A graph-based approach to smart healthcare diagnostics.Journal of applied clinical medical physics · 2026Article
- Explainable Hybrid Deep Learning Framework Integrating MobileNetV2, EfficientNetV2B0, and KNN for MRI-Based Brain Tumor Classification.Cellular and molecular neurobiology · 2026Article
- Probabilistic Neural Networks: An Overview.Methods in molecular biology (Clifton, N.J.) · 2026Review
- Enhancing pancreatic cancer detection in CT images through secretary wolf bird optimization and deep learning.Scientific reports · 2025Article
- Machine learning fusion for glioma tumor detection.Scientific reports · 2025Article
- A hybrid parallel convolutional spiking neural network for enhanced skin cancer detection.Scientific reports · 2025Article
- A hybrid explainable model based on advanced machine learning and deep learning models for classifying brain tumors using MRI images.Scientific reports · 2025Article
- Deep learning-integrated MRI brain tumor analysis: feature extraction, segmentation, and Survival Prediction using Replicator and volumetric networks.Scientific reports · 2025Article
- Efficient and Accurate Brain Tumor Classification Using Hybrid MobileNetV2-Support Vector Machine for Magnetic Resonance Imaging Diagnostics in Neoplasms.Brain sciences · 2024Article
- Intraoperative glioblastoma surgery-current challenges and clinical trials: An update.Cancer pathogenesis and therapy · 2024Review
- Enhancing Winter Wheat Soil-Plant Analysis Development Value Prediction through Evaluating Unmanned Aerial Vehicle Flight Altitudes, Predictor Variable Combinations, and Machine Learning Algorithms.Plants (Basel, Switzerland) · 2024Article
- An effective ensemble learning approach for classification of glioma grades based on novel MRI features.Scientific reports · 2024Article
- Enhanced brain tumor diagnosis using combined deep learning models and weight selection technique.Frontiers in neuroinformatics · 2024Article
- A novel approach to brain tumor detection using K-Means++, SGLDM, ResNet50, and synthetic data augmentation.Frontiers in physiology · 2024Article
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- Computational workflow for discovering small molecular binders for shallow binding sites by integrating molecular dynamics simulation, pharmacophore modeling, and machine learning: STAT3 as case study.Journal of computer-aided molecular design · 2023Article
- AI Deployment on GBM Diagnosis: A Novel Approach to Analyze Histopathological Images Using Image Feature-Based Analysis.Cancers · 2023Article
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- An Intelligent System to Enhance the Performance of Brain Tumor Diagnosis from MR Images.Journal of digital imaging · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification, segmentation and detection of infecting area in brain tumor MRI images are a tedious and time-consuming task. The different anatomy structure of human body can be visualized by an image processing concepts. It is very difficult to have vision about the abnormal structures of human brain using simple imaging techniques. Magnetic resonance imaging technique distinguishes and clarifies the neural architecture of human brain. MRI technique contains many imaging modalities that scans and capture the internal structure of human brain. In this study, we have concentrated on noise removal technique, extraction of gray-level co-occurrence matrix (GLCM) features, DWT-based brain tumor region growing segmentation to reduce the complexity and improve the performance. This was followed by morphological filtering which removes the noise that can be formed after segmentation. The probabilistic neural network classifier was used to train and test the performance accuracy in the detection of tumor location in brain MRI images. The experimental results achieved nearly 100% accuracy in identifying normal and abnormal tissues from brain MR images demonstrating the effectiveness of the proposed technique.
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