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    Spectrally distinct pixel extraction and kernel filtering for brain tumour diagnosis
    (Elsevier Sci Ltd, 2024) Alhudhaif, Adi; Alsubai, Shtwai; Aseeri, Ahmad O.; Nandal, Amita; Polat, Kemal
    Before surgery, medical segmentation is carried out to ascertain the limits of regions of interest (ROI). Critical information may be gathered during the planning phase of the operation if it is permitted that the growth, structure, and behaviour of the ROI be studied. This will increase the probability that the operation will be successful. Most of the time, segmentations are carried out either manually or via machine learning algorithms based on hand annotations. The difference between pure background information and an approximation of such information would affect the performance of brain tumour detection algorithms. This paper offers a technique for the identification of tumour regions that is based on kernel background filtration. The proposed approach presented has a more precise estimation of background information as its primary objective. It is composed of four stages. The first stage includes a probabilistic method based on image denoising. The second stage includes extracting the pure background pixels using a kernel-based technique. The third stage estimates the background covariance matrix using the pixels taken from the pure background pixels. In the fourth phase, we perform segmentation using DCNN. The segmentation procedure begins with training a deep convolutional neural network (DCNN) to recreate damaged brain regions. Window-based methods identify pixels with the largest reconstruction loss. This study isolates tumorous regions using pixel-wise segmentation. The proposed method segments tumours of various sizes with an average Dice score of 0.82.