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Manual diagnosis of brain tumour tissues is particularly labourintensive as well as operator-dependent due to the intricacy of brain tissue. Traditional approaches are ineffective in the presence of these effects, necessitating the assessment of the photographs by professionals who can identify them. This research proposes a novel technique in brain tumour detection based on segmentation with classification utilizing DL architectures. Here, input has been collected as various brain slice image datasets. Initially, this image has been processed for resizing and smoothening and this image has been segmented. The segmentation has been carried out using local binary Gabor fuzzy C-means clustering. Then the segmented image has been classified for spotting the tumour using Berkeley’s wavelet convolutional transfer learning. Based on the accuracy, sensitivity, specificity, Jaccard’s coefficient, spatial overlap, AVME, and FoM, the creative outcome of the approach used was evaluated.

An efficient Berkeley’s wavelet convolutional transfer learning and local binary Gabor fuzzy C-means clustering for brain tumour detection