Efficient Anomaly Identification and Segmentation Using Optimization Based Fuzzy Clustering Approach

A brain tumor represents an accumulation of atypical cells within our brain, which can either be malignant (cancerous) or benign (noncancerous). The method of distinguishing and categorizing these harmful tissues based on their types is known as brain tumor dissection. Magnetic Resonance Imaging (MRI), a prevalent imaging technique, is often employed to capture images of these tumors. However, manually segmenting the tumor from the MRI can be both time-consuming and susceptible to errors. Thus, a quick and accurate method for cerebral tumor delineation is desperately needed. Accurately delineating the sections of a tumor is the goal of segmentation, one of the most difficult tasks in the study of medical images. Deep learning methods have become an increasingly common choice for academics lately because of their outstanding precision and high efficacy for picture segmentation. The widespread use of Fuzzy c-means (FCM) clustering in image segmentation further underscores its effectiveness. Despite the FCM algorithm's impressive computational efficiency and widespread use, it falls short in one key area - it overlooks the spatial information of pixels. This oversight can lead to reduced noise robustness and less precise segmentation. However, this doesn't diminish its overall value; rather, it highlights an area for potential improvement. By incorporating the spatial and intensity data of pixels into Fuzzy C-Means (FCM), we've created an enhanced method known as Weighted FCM (WFCM) that significantly improves the clustering process. Additionally, we've developed a unique bio-inspired optimization algorithm - the Artificial Hummingbird Algorithm (AHB). This algorithm mirrors the exceptional flight abilities and intelligent foraging strategies of hummingbirds in nature. The innovative combination of WFCM and AHB is proving to be highly effective in identifying brain tumors, providing superior segmentation results. This groundbreaking method is revolutionizing medical diagnostics by aiding physicians in making swift disease diagnoses and facilitating informed decisions regarding treatment or surgical procedures.