Optimized Feature Selection for Brain Cancer Detection

A brain cancer is an unexpected growth of nerves within the brain that interferes with the brain's normal function. Numerous lives have been lost as a result of it. It will take time to protect individuals from this illness by prompt discovery and the appropriate treatment. The search for tumor-affected brain cells is a difficult and time-consuming process. However, detecting brain cancer with the precision and speed necessary is a significant hurdle in the field of image processing. This study suggests a brand-new, precise, and enhanced method for finding brain cancer. Preprocessing, segmentation, feature extraction, optimization, and identification are some of the processes the system uses. A skull scripping constitutes one of the first steps in the procedure of finding anomalies in the brain and is used in the initial processing method. Discrete wavelet transform (DWT) is employed for feature extraction, while K-means algorithms are used for picture segmentation. In this section the optimised CNN method is used, which selects the best characteristics via dragon fly optimisation. The CNN classifier is used for identifying brain tumours. Utilising reliability, precision, and recall characteristics, this system evaluates its efficacy with that of another contemporary optimisation approach and declares that its work is superior.