Bayesian Weighted Convolutional Neural Network for Brain Tumor Classification
Keywords:
Bayesian Weighted, Brain Tumor Classification, Convolutional Neural Network (Cnn), Feature Extraction, Long Short-Term MemoryAbstract
Brain Tumor Classification (BTC) in medical image processing is crucial for physicians to make precise diagnoses and treatment decisions. Brain tumours are classified as normal or malignant features by employing Magnetic Resonance Imaging (MRI) images. The extraction of features from the MRI images is crucial, because it identifies the object based on its color, name, shape, and other characteristics. Deep learning methods are currently used in BTC which facing overfitting and vanishing gradient issues. Here, this research is evaluated by the Contrast Enhancement – MRI (CE-MRI) dataset to test the efficiency of the model. After collecting the dataset, pre-processing is done by using min-max normalization which is applied to enhance the quality of the image and the difference between pixel values in the model. After pre-processing, Long Short-Term Memory (LSTM) model is used to extract the features. Finally, the classification stage is done by using Convolutional Neural Network (CNN) to classify the accurate features from the extracted images. For both the extraction and classification stages, optimal weights are updated using Bayesian Weighted (BW) which overcomes the overfitting and vanishing gradient problem in the network. The result analysis, clearly shows that the proposed BW-CNN model has 98.83 % accuracy which is better than the existing Xception model accuracy which has 98.04 %.
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