ArticleBioMed research international2020
A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Hybrid neutrosophic enhanced MobileNetV2 model for leukemia blood cell classification.Frontiers in artificial intelligence · 2026Article
- Ensemble feature selection and tabular data augmentation with generative adversarial networks to enhance cutaneous melanoma identification and interpretability.BioData mining · 2024Article
- NeDSeM: Neutrosophy Domain-Based Segmentation Method for Malignant Melanoma Images.Entropy (Basel, Switzerland) · 2022Article
- Article
- Preoperative Radiomics Analysis of 1p/19q Status in WHO Grade II Gliomas.Frontiers in oncology · 2021Article
- Dynamic PET Imaging Using Dual Texture Features.Frontiers in computational neuroscience · 2021Article
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3 authors.
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Abstract
Discrete wavelet transform (DWT) is often implemented by an iterative filter bank; hence, a lake of optimization of a discrete time basis is observed with respect to time localization for a constant number of zero moments. This paper discusses and presents an improved form of DWT for feature extraction, called Slantlet transform (SLT) along with neutrosophy, a generalization of fuzzy logic, which is a relatively new logic. Thus, a novel composite NS-SLT model has been suggested as a source to derive statistical texture features that used to identify the malignancy of brain tumor. The MR images in the neutrosophic domain are defined using three membership sets, true (
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