ArticleInternational journal of biomedical imaging2017
Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM.
Article in International journal of biomedical imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 1 of them a synthesis that pooled it.
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Who cites it
61 citing papers in PubMed, 1 synthesis or guideline pooled it, 601 citations in OpenAlex.
- A systematic review on deep learning-based automated cancer diagnosis models.Journal of cellular and molecular medicine · 2024Pooled it
- MANet: a multimodal attention convolutional neural network for brain tumor classification.Scientific reports · 2026Article
- Enhancing AI-based decision support system with automatic brain tumor segmentation for EGFR mutation classification.Medical & biological engineering & computing · 2026Article
- Artificial Intelligence-Based Approaches for Brain Tumor Segmentation in MRI: A Review.NMR in biomedicine · 2025Review
- A novel hybrid deep learning model for segmentation and uzzy Res-LeNet based classification for Alzheimer's disease.Neurogenetics · 2025Article
- Hybrid Radiomics and Machine Learning for Brain Tumors Multi-Task Classification: An Exploratory Study on Integrating GLCM and Curvelet-Based Features for Enhanced Accuracy.Health science reports · 2025Article
- A novel approach for the detection of brain tumor and its classification via independent component analysis.Scientific reports · 2025Article
- Secure Hybrid Deep Learning for MRI-Based Brain Tumor Detection in Smart Medical IoT Systems.Diagnostics (Basel, Switzerland) · 2025Article
- Context aware machine learning techniques for brain tumor classification and detection - A review.Heliyon · 2025Review
- Deep learning-integrated MRI brain tumor analysis: feature extraction, segmentation, and Survival Prediction using Replicator and volumetric networks.Scientific reports · 2025Article
- Enhanced local feature extraction of lite network with scale-invariant CNN for precise segmentation of small brain tumors in MRI.PloS one · 2025Article
- Advanced deep learning-based brain tumor classification using a novel customized CNN and optimized residual network.PloS one · 2025Article
- Assessing Glioblastoma Treatment Response Using Machine Learning Approach Based on Magnetic Resonance Images Radiomics: An Exploratory Study.Health science reports · 2025Article
- Prediction of early recurrence of adult-type diffuse gliomas following radiotherapy using multi-modal magnetic resonance images.Medical physics · 2024Article
- Few-Shot Learning for Medical Image Segmentation Using 3D U-Net and Model-Agnostic Meta-Learning (MAML).Diagnostics (Basel, Switzerland) · 2024Article
- Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier.Bioengineering (Basel, Switzerland) · 2024Article
- Article
- Multimodal Brain Tumor Classification Using Convolutional Tumnet Architecture.Behavioural neurology · 2024Article
- ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection.International journal of biomedical imaging · 2024Article
- PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI.Journal of digital imaging · 2023Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The segmentation, detection, and extraction of infected tumor area from magnetic resonance (MR) images are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only. So, the use of computer aided technology becomes very necessary to overcome these limitations. In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated Berkeley wavelet transformation (BWT) based brain tumor segmentation. Furthermore, to improve the accuracy and quality rate of the support vector machine (SVM) based classifier, relevant features are extracted from each segmented tissue. The experimental results of proposed technique have been evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 96.51% accuracy, 94.2% specificity, and 97.72% sensitivity, demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 0.82 dice similarity index coefficient, which indicates better overlap between the automated (machines) extracted tumor region with manually extracted tumor region by radiologists. The simulation results prove the significance in terms of quality parameters and accuracy in comparison to state-of-the-art techniques.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.