ArticleJournal of medical systems2019
Glioma Tumor Grade Identification Using Artificial Intelligent Techniques.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis.Frontiers in oncology · 2022Pooled it
- Deep learning approaches for automated classification and segmentation of head and neck cancers and brain tumors in magnetic resonance images: a meta-analysis study.International journal of computer assisted radiology and surgery · 2021Pooled it
- Context aware machine learning techniques for brain tumor classification and detection - A review.Heliyon · 2025Review
- Article
- A privacy-preserved horizontal federated learning for malignant glioma tumour detection using distributed data-silos.PloS one · 2025Article
- Towards machine learning-based quantitative hyperspectral image guidance for brain tumor resection.Communications medicine · 2024Article
- Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.Cancers · 2024Review
- Association between preoperative white blood cell counts and thirty-day surgical mortality after craniotomy in adult intracranial tumor patients.Frontiers in neurology · 2024Article
- DNA methylation-regulated LINC02587 inhibits ferroptosis and promotes the progression of glioma cells through the CoQ-FSP1 pathway.BMC cancer · 2023Article
- Performance of deep learning algorithms to distinguish high-grade glioma from low-grade glioma: A systematic review and meta-analysis.iScience · 2023Article
- Assessment of artificial intelligence (AI) reporting methodology in glioma MRI studies using the Checklist for AI in Medical Imaging (CLAIM).Neuroradiology · 2023Article
- A deep learning framework integrating MRI image preprocessing methods for brain tumor segmentation and classification.IBRO neuroscience reports · 2022Article
- Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives.Diagnostics (Basel, Switzerland) · 2022Review
- Article
- Up-regulation of LINC00665 contributes to the progression of glioma and correlates with its MRI characteristics.American journal of translational research · 2022Article
- Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review.Frontiers in oncology · 2022Article
- Review
- Review
- Review
- Application of Artificial Intelligence in Diagnosis of Craniopharyngioma.Frontiers in neurology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer aided diagnosis using artificial intelligent techniques made tremendous improvement in medical applications especially for easy detection of tumor area, tumor type and grades. This paper presents automatic glioma tumor grade identification from magnetic resonant images using Wndchrm tool based classifier (Weighted Neighbour Distance using Compound Heirarchy of Algorithms Representing Morphology) and VGG-19 deep convolutional neural network (DNN). For experimentation, DICOM images are collected from reputed government hospital and the proposed intelligent system categorized the tumor into four grades such as low grade glioma, oligodendroglioma, anaplastic glioma and glioblastoma multiform. After preprocessing, features are extracted, optimized and then classified using Windchrm tool where the most significant features are selected on the basis of Fisher score. In the case of DNN classifier, data augmentation is also performed before applying the images into the deep learning network. The performance of the classifiers are analysed with various measures such as accuracy, precision, sensitivity, specificity and F1-score. The results showed reasonably good performance with a maximum classification accuracy of 92.86% for the Wndchrm classifier and 98.25% for VGG-19 DNN classifier. The results are also compared with similar recent works and the proposed system is found to have better performance.
Indexed as
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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.