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Brain cancer importance emanates from the importance of the brain as an organ and its functions. It has a great effect on the whole human body. Identification brain cancer according to its type, it refers to a multiclass classification problem in the machine learning world. In the real-world, object detection and classification face numerous challenges. The object has a large variation in appearances. In this research, a Haar Discrete Wavelet transforms hybrid with the Histogram of Oriented Gradients (HDWT-HOG) features descriptors are proposed by the local gradients in MR image as shape information. The whale optimization algorithm (WOA) plays a great role to reduce the numbers of HOG and Harr features from 38,640 to 120 features only which are less than .01% from all features. This reduction doesn't affect the system performance but it saves time in the classification phase. The test image is matched with its learned class by performing a Bagging ensemble learning classifier. Bagging achieves 96.4% in average accuracy but when Boosting is used, it achieves 95.8%.

Brain Diagnoses Detection Using Whale Optimization Algorithm Based on Ensemble Learning Classifier