Chronology of brain tumor classification of intelligent systems based on mathematical modeling, simulation and image processing techniques
Tumor classification using image processing techniques is becoming a powerful tool nowadays. Based on the importance of this technique, the motivation of this review paper is to present the chronology of brain tumor classification using the digital images and govern the mathematical modeling and sim...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Asian Research Publishing Network
2017
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/81182/1/NormaAlias2017_ChronologyofBrainTumorClassificationofIntelligent.pdf http://eprints.utm.my/id/eprint/81182/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:126244 |
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| Summary: | Tumor classification using image processing techniques is becoming a powerful tool nowadays. Based on the importance of this technique, the motivation of this review paper is to present the chronology of brain tumor classification using the digital images and govern the mathematical modeling and simulation of intelligent systems. The intelligent system involves artificial neural network (ANN), fuzzy logic (FL), support vector machine (SVM), and parallel support vector machine (PSVM). The chronology of brain tumor classification presents the latest part of the literature reviews related to the principal, type and interpretation of segmentation and classification of brain tumors via the large digital dataset from magnetic resonance imaging (MRI) images. This paper has been classified the modeling and simulation in classical and automatic models. Around 115 literature reviews in high ranking journal and high citation index are referred. This paper contains 6 contents, including mathematical modeling, numerical simulation, image processing, numerical results and performance, lastly is the conclusion to standardize the frame concept for the future of chronological framework involving the mathematical modeling and simulation. Research outcome to differentiate the tumor classification based on MRI images, modeling and simulation. Future work outlier in segmentation and classification are given in conclusion. |
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