Rehman Khan Saif Ur
School of Computer Science and Engineering, Central South University, 932 Lushan S Rd, Yuelu District, Changsha, Hunan, China.
Comput Biol Med. 2025 Jun;191:110214. doi: 10.1016/j.compbiomed.2025.110214. Epub 2025 Apr 14.
Kidney irregularities pose a significant public health challenge, often leading to severe complications, yet the limited availability of nephrologists makes early detection costly and time-consuming. To address this issue, we propose a deep learning framework for automated kidney disease detection, leveraging feature fusion and sequential modeling techniques to enhance diagnostic accuracy. Our study thoroughly evaluates six pretrained models under identical experimental conditions, identifying ResNet50 and VGG19 as the highly efficient models for feature extraction due to their deep residual learning and hierarchical representations. Our proposed methodology integrates feature fusion with an inception block to extract diverse feature representations while maintaining imbalance dataset overhead. To enhance sequential learning and capture long-term dependencies in disease progression, ConvLSTM is incorporated after feature fusion. Additionally, Inception block is employed after ConvLSTM to refine hierarchical feature extraction, further strengthening the proposed model ability to leverage both spatial and temporal patterns. To validate our approach, we introduce a new named Multiple Hospital Collected (MHC-CT) dataset, consisting of 1860 tumor and 1024 normal kidney CT scans, meticulously annotated by medical experts. Our model achieves 99.60 % accuracy on this dataset, demonstrating its robustness in binary classification. Furthermore, to assess its generalization capability, we evaluate the model on a publicly available benchmark multiclass CT scan dataset, achieving 91.31 % accuracy. The superior performance is attributed to the effective feature fusion using inception blocks and the sequential learning capabilities of ConvLSTM, which together enhance spatial and temporal feature representations. These results highlight the efficacy of the proposed framework in automating kidney disease detection, providing a reliable, and efficient solution for clinical decision-making. https://github.com/VS-EYE/KidneyDiseaseDetection.git.
肾脏异常给公共卫生带来了重大挑战，常常导致严重并发症，然而肾病专家数量有限，使得早期检测成本高昂且耗时。为解决这一问题，我们提出了一种用于自动检测肾脏疾病的深度学习框架，利用特征融合和序列建模技术提高诊断准确性。我们的研究在相同实验条件下全面评估了六个预训练模型，确定ResNet50和VGG19由于其深度残差学习和层次表示，是高效的特征提取模型。我们提出的方法将特征融合与一个Inception模块相结合，以提取多样化的特征表示，同时控制不平衡数据集的开销。为增强序列学习并捕捉疾病进展中的长期依赖性，在特征融合后引入了卷积长短期记忆网络（ConvLSTM）。此外，在ConvLSTM之后使用Inception模块来优化层次特征提取，进一步增强了所提出模型利用空间和时间模式的能力。为验证我们的方法，我们引入了一个名为多医院收集（MHC - CT）的新数据集，该数据集由1860例肿瘤和1024例正常肾脏CT扫描组成，并由医学专家精心标注。我们的模型在该数据集上实现了99.60%的准确率，证明了其在二分类中的稳健性。此外，为评估其泛化能力，我们在一个公开可用的基准多类CT扫描数据集上对模型进行评估，准确率达到91.31%。卓越的性能归因于使用Inception模块进行的有效特征融合以及ConvLSTM的序列学习能力，它们共同增强了空间和时间特征表示。这些结果突出了所提出框架在自动检测肾脏疾病方面的有效性，为临床决策提供了可靠且高效的解决方案。https://github.com/VS-EYE/KidneyDiseaseDetection.git
