Identification of Brain Tumors using a Combined Approach of Discrete Wavelet Transform and Principal Component Analysis
Keywords:
Multivariate technique, PCA, DWT, RFAbstract
A brain tumor diagnosis is a life-changing event that requires the highest degree of skill and competency from the treating physician. Radiologists must use a certain tumor configuration in order to diagnose brain cancers. This study suggests a technique for distinguishing between normal and abnormal MR brain images. In order to improve the output of cataloging, a three-step technique has been proposed that focuses largely on the presentation of a hybrid feature extraction. A 3-level discrete wavelet transform (DWT) is employed in the first phase of the procedure to extract the image's features. Principal component analysis (PCA) is employed in the second step to cut down on the number of dimensions each feature possesses. To make an accurate diagnosis, a feature selection and a random forest classifier (also known as RF) were applied. With a total of 181 MR brain pictures (81 normal and 100 sick), the experiment findings showed an accuracy of 98%, a sensitivity of 99.2%, and a specificity of 97.8%, demonstrating the efficacy of the prospect technique via evaluation of different forms of writing. The results show that 3L-DWT, PCA, and RF tranquility all contribute to the creation of high-quality cataloging outcomes. The predicted method may be used to the subspecialty categorization of brain MRIs, which would help medical professionals determine if a tumor's progression is normal or pathological.
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