Brain Tumor Detection with Severity Identification Using K-Means Clustering

In the current investigation, we present a methodological framework that utilizes k-means clustering for the identification and measurement of cerebral neoplasms. The principal aim is to precisely as certain the existence of tumors within MRI scans and to evaluate the percentage of tumor involvement within the cerebral tissue. The proposed methodology initiates with the preprocessing of MRI images to enhance contrast and mitigate noise, thus augmenting the efficacy of subsequent analytical processes. Thereafter, k-means clustering is utilized to partition the MRI images into discrete clusters, with each cluster representing various tissue types within the brain. By leveraging the intrinsic intensity disparities between healthy and tumor-afflicted tissues, it becomes feasible to discern the clusters pertaining to tumor tissues. Subsequent to the segmentation phase, advanced post-processing techniques are implemented to refine the segmentation outcomes and extract significant features that are indicative of tumor presence and extent. These features encompass the dimensions, morphology, and spatial distribution of tumor regions within the MRI scans. Through a comprehensive quantitative analysis of these features, it is possible to estimate the percentage of tumor involvement in the brain, thereby furnishing invaluable insights for medical practitioners. Utilizing a dataset of brain MRI scans that encompasses both tumorous and healthy instances, the efficacy of the proposed methodology is evaluated. Furthermore, the estimated percentage of tumor involvement is juxtaposed against the ground truth annotations provided by medical professionals to authenticate the quantitative analysis. Overall, the suggested methodology demonstrates favorable results in the identification and quantification of cerebral tumors via the utilization of k-means clustering. By providing accurate assessments of tumor presence and the extent of involvement, the proposed technique possesses the capacity to assist medical practitioners in making well-informed choices regarding patient diagnosis and therapeutic approaches, ultimately improving patient outcomes and the overall standard of healthcare.