  Brain Tumours (BT) are extremely dangerous and difficult to treat. Currently,
doctors must manually examine images and manually mark out tumour regions to
diagnose BT; this process is time-consuming and error-prone. In recent times,
experts have proposed automating approaches for detecting BT at an early stage.
The poor accuracy and highly incorrect prediction results of these methods
caused them to start the research. In this study, we suggest a fuzzy
logic-based system for categorising BT. This study used a dataset of 253
Magnetic Resonance Imaging (MRI) brain images that included tumour and healthy
images. The images were first pre-processed. After that, we pull out features
like tumour size and the image's global threshold value. The watershed and
region-growing approach is used to calculate the tumour size. After that, the
fuzzy system receives the two features as input. Accuracy, F1-score, precision,
and recall are used to assess the results of the fuzzy by employing both size
determination approaches. With the size input variable discovered by the region
growth method and global threshold values, the fuzzy system outperforms the
watershed method. The significance of this research lies in its potential to
revolutionize brain tumour diagnosis by offering a more accurate and efficient
automated classification system. By reducing human intervention and providing
reliable results, this approach could assist medical professionals in making
timely and precise decisions, leading to improved patient outcomes and
potentially saving lives. The advancement of such automated techniques has the
potential to pave the way for enhanced medical imaging analysis and,
ultimately, better management of brain tumour cases.
