Optimal Deep Convolutional Neural Network Based Automated Brain Tumor Detection on MRI Multi-Modality Images
Keywords:
Brain tumor, MRI Images, Multi-modality, Deep learning, Bayesian optimization, Convolutional neural networkAbstract
Brain tumor (BT) detection utilizing magnetic resonance imaging (MRI) images has made significant progress by leveraging cutting-edge deep learning (DL) approaches. By developing the intricate designs and textures from the images, Convolutional Neural Networks (CNNs) are exposed to significant ability in accurately detecting tumor regions. These networks automatically learn important features in the MRI data, allowing them to differentiate among healthy brain tissue and abnormal regions indicative of tumors. By training on huge databases of annotated MRI images, these methods generalize well and identify tumors across different patient cases. The combination of CNNs with MRI-based tumor recognition not only improves diagnostic accuracy among them expedites the procedure, influences earlier strategies, and enhances patient outcomes. This manuscript offers the Optimal Deep Convolutional Neural Network Automated Brain Tumor Detection and Classification (ODCNN-ABTDC) technique on MRI multi-modality Images. The ODCNN-ABTDC technique mainly focused on the examination of brain MRI images for the classification and detection of BT. To accomplish this, the ODCNN-ABTDC technique involves a series of preprocessing steps to improve the quality of the image. In addition, the features from the preprocessed images are extracted by the use of grey level co-occurrence matrix. For the classification process, the ODCNN-ABTDC technique employs the CNN model which allocates the images into proper classes. Finally, the performance of the CNN model can be boosted by the Bayesian optimization (BO) algorithm. The experimental validation of the ODCNN-ABTDC technique is tested on the benchmark BRATS database. The extensive results demonstrated the greater solution of the ODCNN-ABTDC technique with other recent DL models.
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