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- W4386427269 abstract "Cyber-Physical Systems are Artificial Intelligence based systems in which the tasks are performed accurately by computer-based machine learning algorithms. We know that MRI diagnosis is an important task performed by radiologists. The results need to be extremely accurate and reliable for further diagnosis of the patients. In this paper, we have designed an adaptive Gabor filter bank for MRI Image classification which can help to digitize the process for error-free diagnosis. We have categorized Brain MRI Images into, namely, Glioma, Meningioma, Pituitary and Unaffected tissue using a Support Vector Machine model. Further to validate the accuracy of our model, we have compared the Gabor Filters Bank model with Daubechies (db4) Level Three Filter Model for Image Classification. Gabor Filter Banks with different frequencies and orientation have been applied on the MRI Images to extract the features. Due to its optimum spatial and frequency domain localization properties of Gabor Filters, it is proved that Gabor Filter Bank model gives a better efficiency. SVM classifier along with PCA algorithm is used for classification purpose." @default.
- W4386427269 created "2023-09-05" @default.
- W4386427269 creator A5057249591 @default.
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- W4386427269 date "2023-07-14" @default.
- W4386427269 modified "2023-09-30" @default.
- W4386427269 title "Design of Adaptive Gabor Filter Bank for Feature Extraction of MRI Images and Classification using Support Vector Machine" @default.
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- W4386427269 doi "https://doi.org/10.1109/conecct57959.2023.10234741" @default.
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