Published Paper Details:

One of the intriguing subcategories in the field of image processing is segmentation. As there are so many academics working on image processing segmentation, they are now focusing on Magnetic Resonance Imaging (MRI) segmentations, which is a difficult problem that still requires improvement against noisy data. A popular method for segmenting medical pictures is Fuzzy C Means (FCM), although it only considers the image's intensity and, as a result, yields insufficient results for images with a lot of noise. It is standard practice to design and combine many classifiers using the same fundamental classification learning model using classifiers like bagging and boosting. Boosting models on noiseless data are more robust than bagging. Improving weak learning models significantly reduces errors which only create classifications that are a little better than arbitrary guessing. Results demonstrate the increase in the classification of ac Fuzzy Bee Segmentation method.