Optimization-Enabled Shepard Convolutional Quantum Neural Network for Brain Tumor Detection Using MRI Image

Brain tumors develop when abnormal cells grow within or near the brain. Determining the extent of the tumor is crucial for effective treatment. Magnetic Resonance Imaging (MRI) has emerged as a non-ionizing radiation tool for diagnosing brain tumors. Segmenting brain tumors manually is a tedious process so the performance depends on the experience of the operator. To overcome the above-mentioned problem, in this research project, brain tumor detection is classified by the proposed Shepard Convolutional Quantum Neural Network (ShCQNN) using MRI images. Initially, pre-processing of the input image is carried out with a Mean filter and the process of segmentation is executed by LinkNet. Here, the proposed Chicken Swarm Stock Exchange Trading Optimization (CSSETO) is used to train LinkNet. This CSSETO is formed from Stock Exchange Trading Optimization (SETO) and Chicken Swarm Optimization (CSO). Further, image augmentation includes rotation, random erasing, brightness or contrast adjustment, and shearing. Moreover, the extraction of features is done next to image augmentation, where some important features such as Local Ternary Pattern (LTP), Convolutional Neural Network (CNN), and Local Optimal Oriented Pattern (LOOP) are obtained. In the last stage, a brain tumor is detected using ShCQNN which is the amalgamation of Shepard Convolutional Neural Network (ShCNN) along with Quantum Neural Network (QNN). The two benchmark datasets, namely Multimodal Brain Tumor Segmentation Challenge 2018 (BraTS2018) database and the Figshare dataset are used to assess the performance of the proposed model using performance measures, such as specificity, accuracy, and sensitivity. Also, the performance of the proposed method has been compared with existing models, such as VGG Stacked Classifier Network (VGG-SCNet), Whale Harris Hawks Optimization (WHHO), CNN model, and EfficientNet-B0 and the results revealed that the proposed method provided superior performance than other existing methods. The proposed method obtains the accuracy of 0.925, sensitivity of 0.915, and specificity of 0.915. Regarding the accuracy, the performance improvement of the devised ShCQNN technique is 19.14%, 18.60%, 10.81%, and 2.70% higher than the existing methods VGG-SCNet, WHHO, CNN model, and EfficientNet-B0.