A Weakly Supervised Refinement Framework for Single Image De-Hazing
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Abstract
Both computational intelligence and pattern recognition include brain damage segmentation and classification as essential components. In this procedure, an effective algorithm was used to segment the damaged area and extract the data from the images using characteristics like GLCM. Based fuzzy C-means clustering technique (M-FCM) is suggested for clustering during the segmentation process. In order to categorise the severity of the brain input, a procedure to identify brain lesions is used in the medical area, along with a way to classify its characteristics using KNN. The major goals of this method are to locate the malignant area on an MRI of the brain and to categorise the severity of that brain in order to increase process efficiency.
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