Background: Segmenting images is the most difficult as well as a
happening research topic in the domain of image processing. Despite accessibility to
a huge number of excellent approaches for brain Magnetic Resonance Imaging (MRI)
segmentations, it remains a difficult job and speed of the technique requires great
improvements. Medical images segmentations need effective as well as strong
segmentation models that are robust against noisy data.
Method: In the current work, the suggested Fuzzy C-Means (FCM) is a widely
utilized approach to segment medical images though it regards merely image
intensity and hence, provides poor outcomes for noise-filled images. Classifiers like
Bagging as well as Boosting are common resampling ensemble approaches creating
and merging several classifiers through the same learning model for base classifiers. Boosting models are
more robust than bagging ones on noiseless data. Boosting decreases errors of weak learning models
which create classifiers that are merely a little better than arbitrary guesses.
Results: Results show that the Fuzzy Bee Segmentation Bagging method increased classification
accuracy by 4.34%, 3.02% & 1.71% when compared with FCM Segmentation- Boosting, FCM
Segmentation - Bagging and Fuzzy Bee Segmentation - Boosting methods.
Conclusion: The final images with their details are very much helpful for further brain MRI image
processing and analysis in medical diagnosis. Results show that the classification accuracy, precision as
well as recall, was better than all other methods regarded for experiments.