Performance Evaluation of Brain Tumor Identification and Examination Using MRI Images with Innovative Convolution Neural Networks and Comparing the Accuracy with RNN Algorithm

Abstract

The main aim of the paper is to find the accuracy for brain tumor detection using the Innovative CNN and RNN algorithms. The paper addresses the design and implementation of brain tumor detection with an accurate prediction. Materials and Methods: Innovative Convolutional Neural Networks and Recurrent Neural Networks are used for finding the accuracy of brain tumor detection. Data models were trained with the neural network algorithms where the brain tumor model adopts the data models and gives responses by adopting those effectively. The model checks patterns for providing the responses to the users by using a pattern matching module. Accuracy calculation was done by using neural network algorithms. Results: The accuracy of Innovative Convolutional Neural Network in brain tumor detection is more significantly improved which is more than 95% (approx.) than the Recurrent Neural Networks. Conclusion: Based on Independent T-test analysis using SPSS statistical software, the innovative Convolutional Neural Network algorithm is significant and has more accuracy compared to Recurrent Neural Networks.