Abstract
Brain Tumor (BT) is the deadliest tumors found globally, which commonly develop in both adults and children. Due to the variety of tumor cells is difficult to categorize a large variety of tumor types, which also leads to more complexity. The key intention of this work is to build and create a useful approach for classifying BT using a hybrid Quantum Dilated Convolutional Neural Network-Deep Maxout Network (QDCNN-DMN). Here, the Magnetic Resonance Imaging (MRI) is used as the input to the image enhancement framework, where it is sharpened using logarithmic transformations. Deep Embedded Clustering (DEC) is used in the skull-stripping process. After that, tumor segmentation is done by the Structure Correcting Adversarial Network (SCAN), and then, the statistical and some other important features are extracted. Subsequently, BT detection and classification are done by the proposed hybrid QDCNN-DMN. The QDCNN-DMN is framed by the incorporation of Quantum Dilated Convolutional Neural Network (QDCNN) and Deep Maxout Network (DMN). It is demonstrated that QDCNN-DMN observed superior specificity, sensitivity, and accuracy with 93.7%, 94.5% and 94.0% based on the Figshare dataset for the BT classification.
Published Version
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