Diagnosis of Brain Tumors in MR Images Using Metaheuristic Optimization Algorithms - Braik, Malik; Sheta, Alaa; Aljahdali, Sultan (Innovation in Information Systems and Technologies to Support Learning Research, 2020)
Image clustering presents a hot topic that researchers have chased extensively. There is always a need to a promising clustering technique due to its vital role in further image processing steps. This paper presents a compelling clustering approach for brain tumors and breast cancer in Magnetic Resonance Imaging (MRI). Driven by the superiority of nature-inspired algorithms in providing computational tools to deal with optimization problems, we propose Flower Pollination Algorithm (FPA) and Crow Search Algorithm (CSA) to present a clustering method for brain tumors and breast cancer. Evaluation clustering results of CSA and FPA were judged using two apposite criteria and compared with results of K-means, fuzzy c-means and other metaheuristics when applied to cluster the same benchmark datasets. The clustering method-based CSA and FPA yielded encouraging results, significantly outperforming those obtained by K-means and fuzzy c-means and slightly surpassed those of other metaheuristic algorithms.
Keywords: Clustering; Crow Search Algorithm; Flower Pollination Algorithm; Fuzzy C-mean; K-means; dept.csc