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Gliomas, the most prevalent primary brain tumors, require accurate segmentation for diagnosis and risk assessment. In this paper, we develop a novel deep learning-based method, the Dynamic Hierarchical Attention for Improved Segmentation and Survival Prognosis (DHA-ISSP) model. The DHA-ISSPmodel combines a three-band 3D convolutional neural network (CNN) U-Net architecture with dynamic hierarchical attention mechanisms, enabling precise tumor segmentation and survival prediction. The DHA-ISSP model captures fine-grained details and contextual information by leveraging attention mechanisms at multiple levels, enhancing segmentation accuracy. By achieving remarkable results, our approach surpasses 369 competing teams in the 2020 Multimodal Brain Tumor Segmentation Challenge. With a Dice similarity coefficient of 0.89 and a Hausdorff distance of 4.8 mm, the DHA-ISSP model demonstrates its effectiveness in accurately segmenting brain tumors. We also extract radiomic characteristics fromthe segmented tumor areas using theDHA-ISSPmodel. By applying cross-validation of decision trees to the selected features, we identify crucial predictors for glioma survival, enabling personalized treatment strategies. Utilizing the DHA-ISSP model and the desired features, we assess patients’ overall survival and categorize survivors into short, mid, in addition to long survivors. The proposed work achieved impressive performance metrics, including the highest accuracy of 0.91, precision of 0.84, recall of 0.92, F1 score of 0.88, specificity of 0.94, sensitivity of 0.92, area under the curve (AUC) value of 0.96, and the lowest mean absolute error value of 0.09 andmean squared error value of 0.18. These results clearly demonstrate the superiority of the proposed system in accurately segmenting brain tumors and predicting survival outcomes, highlighting its significant merit and potential for clinical applications. [ABSTRACT FROM AUTHOR]

In recent days, Deep Learning (DL) techniques have become an emerging transformation in the field of machine learning, artificial intelligence, computer vision, and so on. Subsequently, researchers and industries have been highly endorsed in the medical field, predicting and controlling diverse diseases at specific intervals. Liver tumor prediction is a vital chore in analyzing and treating liver diseases. This paper proposes a novel approach for predicting liver tumors using Convolutional Neural Networks (CNN) and a depth-based variant search algorithm with advanced attention mechanisms (CNN-DS-AM). The proposed work aims to improve accuracy and robustness in diagnosing and treating liver diseases. The anticipated model is assessed on a Computed Tomography (CT) scan dataset containing both benign and malignant liver tumors. The proposed approach achieved high accuracy in predicting liver tumors, outperforming other state-of-the-art methods. Additionally, advanced attention mechanisms were incorporated into the CNN model to enable the identification and highlighting of regions of the CT scans most relevant to predicting liver tumors. The results suggest that incorporating attention mechanisms and a depth-based variant search algorithm into the CNN model is a promising approach for improving the accuracy and robustness of liver tumor prediction. It can assist radiologists in their diagnosis and treatment planning. The proposed system achieved a high accuracy of 95.5% in predicting liver tumors, outperforming other state-of-the-art methods. [ABSTRACT FROM AUTHOR]