MRI brain tumor classification using robust Convolutional Neural Network CNN approach

During the last decade, the deep learning depending upon the Convolutional Neural Network (CNN) structure has been demonstrated as an efficient approach to classify different objects properly, and it has wide significant advancements in computer vision algorithms as well as increases in processing hardware. This research work aims to utilize the developed and evaluated Magnetic Resonance Imaging (MRI) technique for the predication of brain tumor employing simplified Convolutional Neural Network CNN structure. The developed approach uses less memory and processing power while handling huge datasets such as Real Brain Dataset. The recognition algorithm can rapidly distinguish an object within few seconds, and the maximum accuracy reaches to 98% for a good CNN structure setting option. The model being fit to the data till no additional remarkable reduction in the loss function value was performed upon the data of confirmation. It was trained and confirmed till (100) epochs upon the precision of the 70% of the (154) image samples and the testing of 30% image samples correspondingly with 10-cross fold validation. The measured classification accuracy of the brain tumor at the last epoch of the classifier training of validation data is virtuous.