A Novel Fusion Approach to Detect Brain Tumor Using Machine Learning for MRI Images

Abstract

Medical Imaging challenges the recent researchers with variability of potential structures, positions and appearance strengths of various tumors present among the patients. The proposed work presents an effective brain tumor watershed segmentation technique created on 2D image followed by statistical feature extraction. Machine Learning models such as SVM, KNN, and XG boost were used to inspire the network design in order to extract tumor existence. The proposed segmentation algorithm has been tested and evaluated on original images that consist of an aggregate of 52 normal MRI volumes of distinctive patients with the presence of tumors or not signifying distinctive structures that obtains outcomes near to physical segmentation implementations. The novelty present in the proposed work classifies whether the tumor is present or not with an accuracy of approximately 98%.

Wavelet Theories and Techniques for Brain Disease: Diagnosis and Treatmentz

“…Wavelet analysis aids in the detection of brain diseases through denoising by separating noise from brain signals, employing thresholding techniques, enhancing the SNR, and ultimately improving the quality of the data for accurate diagnostics [27]. This denoising process is invaluable in neuroimaging, as it allows for more reliable and sensitive detection of neurological abnormalities, facilitating early diagnosis and intervention for brain diseases.…”