A brain tumor is a medical disorder faced by individuals of all demographics. Medically, it is described as the spreadof non-essential cells close to or throughout the brain. Symptoms of this ailment include headaches, seizures, andsensory changes. This research explores two main categories of brain tumors: benign and malignant. Benignspreads steadily, and malignant express growth makes it dangerous. Early identification of brain tumors is a crucialfactor for the survival of patients. This research provides a state-of-the-art approach to the early identification oftumors within the brain. We implemented the SegResNet architecture, a widely adopted architecture for three-dimensional segmentation, and trained it using the automatic multi-precision method. We incorporated the diceloss function and dice metric for evaluating the model. We got a dice score of 0.84. For the tumor core, we got adice score of 0.84; for the whole tumor, 0.90; and for the enhanced tumor, we got a score of 0.79.
Facial expression recognition is a vital research topic in most fields ranging from artificial intelligence and gamingto human-computer interaction (HCI) and psychology. This paper proposes a hybrid model for facial expressionrecognition, which comprises a deep convolutional neural network (DCNN) and a Haar Cascade deep learningarchitecture. The objective is to classify real-time and digital facial images into one of the seven facial emotioncategories considered. The DCNN employed in this research has more convolutional layers, ReLU activationfunctions, and multiple kernels to enhance filtering depth and facial feature extraction. In addition, a HaarCascade model was also mutually used to detect facial features in real-time images and video frames. Grayscaleimages from the Kaggle repository (FER2013) and then exploited graphics processing unit (GPU) computation toexpedite the training and validation process. Pre-processing and data augmentation techniques are applied toimprove training efficiency and classification performance. The experimental results show a significantly improvedclassification performance compared to state-of-the-art (SoTA) experiments and research. Also, compared to otherconventional models, this paper validates that the proposed architecture is superior in classification performancewith an improvement of up to 6%, totaling up to 70% accuracy, and with less execution time of 2,098.8 s
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