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... weighted IOU defines how many pixels of each class are weighted in the disproportion pixel class to prevent the larger class from overlapping the smaller class. Table 1 shows the segmentation performance parameters of the proposed and SegNet5 CNN models. Therefore, the developed U-Net CNN architecture is introduced by modifying the upper/lower sampling module to replace the upper/layer pooling layer of the current U-Net architecture. ...Similar publications
Brain tumor segmentation in multimodal MRI has great significance in clinical diagnosis and treatment. The utilization of multimodal information plays a crucial role in brain tumor segmentation. However, most existing methods focus on the extraction and selection of deep semantic features, while ignoring some features with specific meaning and impo...
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This study proposes a deep learning model for the classification and segmentation of brain tumors from magnetic resonance imaging (MRI) scans. The classification model is based on the EfficientNetB1 architecture and is trained to classify images into four classes: meningioma, glioma, pituitary adenoma, and no tumor. The segmentation model is based on the U-Net architecture and is trained to accurately segment the tumor from the MRI images. The models are evaluated on a publicly available dataset and achieve high accuracy and segmentation metrics, indicating their potential for clinical use in the diagnosis and treatment of brain tumors.
