ArticleSpringerPlus2015
Pathological brain detection in MRI scanning by wavelet packet Tsallis entropy and fuzzy support vector machine.
Article in SpringerPlus, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- CEPS: An Open Access MATLAB Graphical User Interface (GUI) for the Analysis of Complexity and Entropy in Physiological Signals.Entropy (Basel, Switzerland) · 2021Article
- Two-stepped majority voting for efficient EEG-based emotion classification.Brain informatics · 2020Article
- Article
- A Multilayer Perceptron Based Smart Pathological Brain Detection System by Fractional Fourier Entropy.Journal of medical systems · 2016Article
- Regional Image Features Model for Automatic Classification between Normal and Glaucoma in Fundus and Scanning Laser Ophthalmoscopy (SLO) Images.Journal of medical systems · 2016Article
- Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning.Frontiers in computational neuroscience · 2016Article
- Parallel Digital Watermarking Process on Ultrasound Medical Images in Multicores Environment.International journal of biomedical imaging · 2016Article
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Authors and funding
7 authors.
Funding
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Abstract
An computer-aided diagnosis system of pathological brain detection (PBD) is important for help physicians interpret and analyze medical images. We proposed a novel automatic PBD to distinguish pathological brains from healthy brains in magnetic resonance imaging scanning in this paper. The proposed method simplified the PBD problem to a binary classification task. We extracted the wavelet packet Tsallis entropy (WPTE) from each brain image. The WPTE is the Tsallis entropy of the coefficients of the discrete wavelet packet transform. The, the features were submitted to the fuzzy support vector machine (FSVM). We tested the proposed diagnosis method on 3 benchmark datasets with different sizes. A ten runs of K-fold stratified cross validation was carried out. The results demonstrated that the proposed WPTE + FSVM method excelled 17 state-of-the-art methods w.r.t. classification accuracy. The WPTE is superior to discrete wavelet transform. The Tsallis entropy performs better than Shannon entropy. The FSVM excels standard SVM. In closing, the proposed method "WPTE + FSVM" is effective in PBD.
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