Combining CNN Features with Voting Classifiers for Optimizing Performance of Brain Tumor Classification

Simple Summary This study presents a hybrid model for brain tumor detection. Contrary to manual featur extraction, features extracted from a convolutional neural network are used to train the model. Experimental results show the efficacy of CNN features over manually extracted features and model can detect brain tumor with a 99.9% accuracy. Brain tumors and other nervous system cancers are among the top ten leading fatal diseases. The effective treatment of brain tumors depends on their early detection. This research work makes use of 13 features with a voting classifier that combines logistic regression with stochastic gradient descent using features extracted by deep convolutional layers for the efficient classification of tumorous victims from the normal. From the first and second-order brain tumor features, deep convolutional features are extracted for model training. Using deep convolutional features helps to increase the precision of tumor and non-tumor patient classification. The proposed voting classifier along with convoluted features produces results that show the highest accuracy of 99.9%. Compared to cutting-edge methods, the proposed approach has demonstrated improved accuracy.