An Innovative Data-Driven Technique for Identifying Brain Disorders Using a Combined Wavelet Transform Method
Keywords:
Feature extraction; Discrete Wavelet Transform; Support Vector Machine (SVM), Multi-Layer Perceptron Neural Network (MLPNN), Gaussian Naïve Bayesian Network and Logistic RegressionAbstract
Feature extraction is a vital technique within the fields of machine learning and data mining, especially when it
comes to image analysis and classification. It involves isolating the most significant features from an image, which play
a crucial role in enabling accurate categorization. As a fundamental pre-processing stage, the efficiency of any
classification algorithm is highly influenced by the quality and relevance of the extracted numerical features that represent
the image data. This study presents a combined wavelet-based one feature extraction method for identifying abnormalities
in the brain using MRI data is the Discrete Wavelet Transform (DWT). The investigation specifically focu
References
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