Pimpalkar Amit, Saini Dilip Kumar Jang Bahadur, Shelke Nilesh, Balodi Arun, Rapate Gauri, Tolani Manoj
School of Computer Science and Engineering, Ramdeobaba College of Engineering and Management, Ramdeobaba University, Nagpur, Maharashtra, India.
Department of Computer Science and Engineering (Cyber Security), School of Engineering, Dayananda Sagar University, Bangalore, India.
Sci Rep. 2025 Mar 28;15(1):10741. doi: 10.1038/s41598-025-94905-2.
The kidney plays a vital role in maintaining homeostasis, but lifestyle factors and diseases can lead to kidney failures. Early detection of kidney disease is crucial for effective intervention, often challenging due to unnoticeable symptoms in the initial stages. Computed tomography (CT) imaging aids specialists in detecting various kidney conditions. The research focuses on classifying CT images of cysts, normal states, stones, and tumors using a hyperparameter fine-tuned approach with convolutional neural networks (CNNs), VGG16, ResNet50, CNNAlexnet, and InceptionV3 transfer learning models. It introduces an innovative methodology that integrates finely tuned transfer learning, advanced image processing, and hyperparameter optimization to enhance the accuracy of kidney tumor classification. By applying these sophisticated techniques, the study aims to significantly improve diagnostic precision and reliability in identifying various kidney conditions, ultimately contributing to better patient outcomes in medical imaging. The methodology implements image-processing techniques to enhance classification accuracy. Feature maps are derived through data normalization and augmentation (zoom, rotation, shear, brightness adjustment, horizontal/vertical flip). Watershed segmentation and Otsu's binarization thresholding further refine the feature maps, which are optimized and combined using the relief method. Wide neural network classifiers are employed, achieving the highest accuracy of 99.96% across models. This performance positions the proposed approach as a high-performance solution for automatic and accurate kidney CT image classification, significantly advancing medical imaging and diagnostics. The research addresses the pressing need for early kidney disease detection using an innovative methodology, highlighting the proposed approach's capability to enhance medical imaging and diagnostic capabilities.
肾脏在维持体内平衡方面发挥着至关重要的作用，但生活方式因素和疾病可能导致肾衰竭。早期发现肾脏疾病对于有效干预至关重要，然而由于在初始阶段症状不明显，这一过程往往具有挑战性。计算机断层扫描（CT）成像有助于专家检测各种肾脏疾病。该研究聚焦于使用卷积神经网络（CNN）、VGG16、ResNet50、CNNAlexnet和InceptionV3迁移学习模型的超参数微调方法，对囊肿、正常状态、结石和肿瘤的CT图像进行分类。它引入了一种创新方法，将微调迁移学习、先进的图像处理和超参数优化相结合，以提高肾脏肿瘤分类的准确性。通过应用这些复杂技术，该研究旨在显著提高识别各种肾脏疾病的诊断精度和可靠性，最终在医学成像中为患者带来更好的治疗效果。该方法实施图像处理技术以提高分类准确性。通过数据归一化和增强（缩放、旋转、剪切、亮度调整、水平/垂直翻转）得出特征图。分水岭分割和大津法二值化阈值进一步细化特征图，并使用 Relief 方法对其进行优化和组合。采用了广泛的神经网络分类器，各模型实现了高达99.96%的最高准确率。这一性能使所提出的方法成为自动且准确的肾脏CT图像分类的高性能解决方案，显著推动了医学成像和诊断技术的发展。该研究使用创新方法满足了早期肾脏疾病检测的迫切需求，突出了所提出方法增强医学成像和诊断能力的能力。
