IMPROVING THE EFFICIENCY OF DETECTING AND CATEGORIZING BRAIN TUMORS FROM DIVERSE MRI IMAGES THROUGH THE IMPLEMENTATION IN DEEP CONVOLUTION NEURAL NETWORK
DOI:
https://doi.org/10.11113/aej.v15.23083Keywords:
Brain Tumor, Local Brain images, Xception DCNN model, MRI, Classification accuracyAbstract
Deep learning is a popular and effective approach to medical imaging detection and classification. Various brain tumor classifications are related to an ongoing research topic made possible by the diversity of cancer features. Local brain images testing in the latest brain images datasets BRAT can be challenging due to their processing time, lower accuracy, and the possibility of overfitting and underfitting. In this paper, Xception DCNN model can be implemented to test the local images or real images and apply three datasets in this model, analyse the individual result and improve the training speed and stability of neural networks by normalizing the activations of each layer. The Xception model can significantly reduce the number of parameters and computational complexity without compromising the model’s accuracy. Moreover, this system used popular benchmark datasets such as Kaggle and BRATS, the suggested research MRI image datasets were assessed, and the local validation dataset was proposed. In this research work, this model achieved the best performance results with training accuracy and loss 99±0.005%, validation accuracy and loss 98±0.2%. The main objectives are improving the accuracy of classification, avoiding overestimation along with underestimating, and decreasing down on processing time.
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