Rao P Kiran, Chatterjee Subarna, Janardhan M, Nagaraju K, Khan Surbhi Bhatia, Almusharraf Ahlam, Alharbe Abdullah I
Artificial Intelligence, Department of Computer Science and Engineering, Ravindra College of Engineering for Women, Kurnool 518001, India.
Department of Computer Science and Engineering, Faculty of Engineering, MS Ramaiah University of Applied Sciences, Bengaluru 560058, India.
Diagnostics (Basel). 2023 Oct 18;13(20):3244. doi: 10.3390/diagnostics13203244.
Kidney tumors represent a significant medical challenge, characterized by their often-asymptomatic nature and the need for early detection to facilitate timely and effective intervention. Although neural networks have shown great promise in disease prediction, their computational demands have limited their practicality in clinical settings. This study introduces a novel methodology, the UNet-PWP architecture, tailored explicitly for kidney tumor segmentation, designed to optimize resource utilization and overcome computational complexity constraints. A key novelty in our approach is the application of adaptive partitioning, which deconstructs the intricate UNet architecture into smaller submodels. This partitioning strategy reduces computational requirements and enhances the model's efficiency in processing kidney tumor images. Additionally, we augment the UNet's depth by incorporating pre-trained weights, therefore significantly boosting its capacity to handle intricate and detailed segmentation tasks. Furthermore, we employ weight-pruning techniques to eliminate redundant zero-weighted parameters, further streamlining the UNet-PWP model without compromising its performance. To rigorously assess the effectiveness of our proposed UNet-PWP model, we conducted a comparative evaluation alongside the DeepLab V3+ model, both trained on the "KiTs 19, 21, and 23" kidney tumor dataset. Our results are optimistic, with the UNet-PWP model achieving an exceptional accuracy rate of 97.01% on both the training and test datasets, surpassing the DeepLab V3+ model in performance. Furthermore, to ensure our model's results are easily understandable and explainable. We included a fusion of the attention and Grad-CAM XAI methods. This approach provides valuable insights into the decision-making process of our model and the regions of interest that affect its predictions. In the medical field, this interpretability aspect is crucial for healthcare professionals to trust and comprehend the model's reasoning.
肾肿瘤是一项重大的医学挑战，其特点是往往无症状，需要早期检测以便及时进行有效干预。尽管神经网络在疾病预测方面显示出巨大潜力，但其计算需求限制了它们在临床环境中的实用性。本研究引入了一种新颖的方法，即UNet-PWP架构，专门为肾肿瘤分割量身定制，旨在优化资源利用并克服计算复杂性限制。我们方法的一个关键新颖之处是应用了自适应分区，它将复杂的UNet架构解构为较小的子模型。这种分区策略降低了计算需求，并提高了模型处理肾肿瘤图像的效率。此外，我们通过合并预训练权重来增加UNet的深度，从而显著提高其处理复杂和详细分割任务的能力。此外，我们采用权重修剪技术来消除冗余的零权重参数，进一步简化UNet-PWP模型而不影响其性能。为了严格评估我们提出的UNet-PWP模型的有效性，我们与DeepLab V3+模型进行了比较评估，两者均在“KiTs 19、21和23”肾肿瘤数据集上进行训练。我们的结果令人乐观，UNet-PWP模型在训练和测试数据集上均达到了97.01%的优异准确率，在性能上超过了DeepLab V3+模型。此外，为确保我们模型的结果易于理解和解释。我们纳入了注意力和Grad-CAM XAI方法的融合。这种方法为我们模型的决策过程以及影响其预测的感兴趣区域提供了有价值的见解。在医学领域，这种可解释性对于医疗保健专业人员信任和理解模型的推理至关重要。
