TFOPT-WEQN: OPTIMIZED WEIGHTED EUCLIDEAN LOSS-ENABLED QUANTUM CONVOLUTIONAL NEURAL NETWORK FOR BRAIN TUMOR DETECTION AND CLASSIFICATION

Brain tumor is a debilitating disease causing irregular growth of brain cells insisting on early tumor diagnosis for treatment planning to enhance the patients’ survival rate. Due to the miniature tumor areas, brain tumor segmentation has proven to be a challenging issue, even for neural networks. Further, the experienced radiologist delineates the tumor regions due to noise present in MRI images. Specifically, the tiny size of the tumor locations and the significant differences in area occupancy between different tumors make it impossible to identify the tumor regions. To avoid such constraints, this research proposes a trail and flee optimization-based weighted Euclidean loss-enabled quantum convolutional neural network (TFOpt-WEQN) model for effective brain tumor detection. Here, the WEQN model is integrated with hybrid weighted categorical cross entropy and Euclidean distance-based loss function to determine the significant tumor regions using featured datasets. Moreover, the TFOpt algorithm is developed by combining Harris Hawks and Red Kite optimizations to tune the model while training. In this research, the segmentation using visual geometry group-16 enabled eigen-cam-full grad-based hybrid U-Network (VE 2 CFG-hyUNet) model plays a key role in separating tumor regions from the MRI images. The proposed model ultimately overcomes the major limitations of previous models and accurately detects the tumor regions utilizing the BraTS dataset consisting of four MRI modalities including the FLAIR, T1, T2, and T1C for diagnosis. The experimental results demonstrate that the TFOpt-WEQN model greatly achieves maximum accuracy, positive predicted value, negative predicted value, and F1-score of 95.66%, 0.96, 0.95, and 95.72% for the BraTS-2020 dataset exceeding the other state-of-the-art methods.