CANCER METASTASES IN THE BRAIN BASED ON FUZZY GENETIC ALGORITHM AND ARTIFICIAL NEURAL NETWORK

Abstract

Cancer cells can metastasize to the brain when they move from their primary site. The symptoms of metastases are influenced by the location, size, and number of growths within the brain as well as the degree of swelling. The growing metastatic brain tumors are putting pressure on the nearby brain tissue. Therefore, among the common symptoms are headaches, the inability to move an arm or a leg, sleepiness, memory loss, personality changes, seizures, and nausea or vomiting, most frequently happening. After the age of 45, the risk of developing a metastatic brain tumor rises, reaching its peak in people over the age of 65. The newly proposed method is to extract the two various metastasised lung and melanoma cancers from brain metastases. The most dangerous cancer is lung cancer which develops from the lung tissue, and the most dangerous type of skin cancer is melanoma which can appear anywhere on the body. Hence, currently, a novel system for MRI is utilized to find the rapid growth of brain metastases cancer cells in aberrant tissue that is lung cancer (LC) or Melanoma Cancer (MC). Hence a hybrid technique for automatic lung cancer brain metastases (LCBM) and Melanoma Cancer brain metastases (MCBM) detection using simultaneous feature selection with a Fuzzy Genetic Algorithm (FGA) and Artificial Neural Network (ANN) is proposed here to show the classification performance analysis to solve regression problems. The suggested technique uses Rtool and an MRI clinical dataset to identify the affected region of brain cancer metastases.