Background: The major challenge in medical imaging is to achieve high accuracy output during semantic image segmentation tasks in biomedical imaging while having fewer computational operations and faster inference. It is necessary in medical modalities such as kidney tumor CT scan activities, to assist radiologists. Several previous studies have carried out a complex deep network that requires high computational resources. However, a deep network on semantic segmentation of kidney tumor CT scans with fewer flops and parameters has not yet been evaluated.Methods: This research paper presents a novel network model called Weight Pruning U-Net (WP-UNet) which is extremely fast, compact, and computationally efficient to address this problem with kidney tumor CT scan images as an application. Results We apply the proposed deep network model on the kidney tumor CT scan image dataset on computational devices with limited resources for computing. We build a CNN model with minimum parameters inspired by the commonly adapted U-Net architecture of the deep convolution neural network model for CT scan image analysis by making use of a depthwise separable convolution functional layer in the entire network model. We proposed weight pruning with the depthwise separable and batch normalized UNet model to reach the expected performance and reduce the loss in the process. WP-UNet has 3 major benefits,- : (a) a lightweight model with a smaller size (b) fewer parameters, and (c) a faster assumption time with a less than floating point calculation with computational complexity (FLOPs). WP-UNet was tested on the KiTs challenge Biomedical CT Scan imaging Dataset for kidney tumor semantic segmentation (KiTs), and the results showed that comparable and often better results were obtained by the WP-UNet model compared to the existing state-of-the-art models.Conclusions: Unlike previous assumptions, our findings indicate that the architecture proposed is smaller than U-Net and demands 3x less computational complexity while retaining respectable accuracy results. Moreover it affects kidney tumor medical image analysis and their practical application.

Chronic Kidney Disease (CKD) represents a considerable global health challenge, emphasizing the need for precise and prompt prediction of disease progression to enable early intervention and enhance patient outcomes. As per this study, we introduce an innovative fusion deep learning model that combines a Graph Neural Network (GNN) and a tabular data model for predicting CKD progression by capitalizing on the strengths of both graph-structured and tabular data representations. The GNN model processes graph-structured data, uncovering intricate relationships between patients and their medical conditions, while the tabular data model adeptly manages patient-specific features within a conventional data format. An extensive comparison of the fusion model, GNN model, tabular data model, and a baseline model was conducted utilizing various evaluation metrics, encompassing accuracy, precision, recall, and F1-score. The fusion model exhibited outstanding performance across all metrics, underlining its augmented capacity for predicting CKD progression. The GNN model’s performance closely trailed the fusion model, accentuating the advantages of integrating graph-structured data into the prediction process. Hyperparameter optimization was performed using grid search, ensuring a fair comparison among the models. The fusion model displayed consistent performance across diverse data splits, demonstrating its adaptability to dataset variations and resilience against noise and outliers. In conclusion, the proposed fusion deep learning model, which amalgamates the capabilities of both the GNN model and the tabular data model, substantially surpasses the individual models and the baseline model in predicting CKD progression. This pioneering approach provides a more precise and dependable method for early detection and management of CKD, highlighting its potential to advance the domain of precision medicine and elevate patient care.

Background Accurate semantic segmentation of kidney tumours in computed tomography (CT) images is difficult because tumours feature varied forms and, occasionally, look alike. The KiTs19 challenge sets the groundwork for future advances in kidney tumour segmentation. Methods We present WP-UNet, a deep network model that is lightweight with a small scale; it involves few parameters with a quick assumption time and a low floating-point computational complexity. Results We trained and evaluated the model with CT images from 300 patients. Thefindings implied the dominance of our method on the training Dice score (0.98) for the kidney tumour region. The proposed model only uses 1,297,441 parameters and 7.2e FLOPS, three times lower than those for other network models. Conclusions The results confirm that the proposed architecture is smaller than that of U-Net, involves less computational complexity, and yields good accuracy, indicating its potential applicability in kidney tumour imaging.

No abstract