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<p>Renal cortical abnormalities frequently signify severe kidney conditions, rendering their precise diagnosis crucial for clinical management and treatment strategy formulation. Nonetheless, manual evaluation of nuclear renal imaging is arduous and prone to considerable inter-observer variability, resulting in conflicting results. This paper presents a fully automated method for the differential detection of renal cortical anomalies via deep learning-based segmentation and classification. Initially, we generated an innovative compilation of rigorously annotated renal nuclear images from 613 patients. Among them 193 patients are primarily diagnosed with kidney scar. Utilizing this dataset, we devised a novel segmentation method to precisely identify and outline renal areas. The proposed DenseNet121_Self-ONN_FPN model combines the DenseNet121 backbone, Self-Organizing Neural Network (Self-ONN) layers in the Feature Pyramid Network (FPN) for enhanced performance in segmentation tasks achieving impressive results: an Accuracy of 98.74%, Intersection over Union (IoU) of 86.47%, Dice Similarity Coefficient (DSC) of 92.74%, precision of 92.61%, recall of 92.88%, F1-score of 99.29%, False Negative Rate (FNR) of 7.12%, and False Positive Rate (FPR) of 0.71%. We optimized a modified DenseNet205 model for the classification of renal cortical anomalies. We employed Contrast Limited Adaptive Histogram Equalization (CLAHE) and Gamma correction as a pre-processing measure to enhance image contrast and model efficacy. The model attained exceptional results, exceeding state-of-the-art techniques with an accuracy of 96.91%, precision of 96.98%, sensitivity of 96.91%, F1-score of 96.86%, and specificity of 95.87%. Furthermore, we used ScoreCAM explainable AI to produce heatmaps for the classification network, offering critical insights into the model’s decision-making process and guaranteeing transparency for clinical application. This automated pipeline overcomes the constraints of manual image analysis by improving precision, efficiency, and reliability, while establishing a new standard for renal segmentation and classification.</p>

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Can deep learning-based segmentation and classification improve the detection of renal cortical abnormalities?

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月22日

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  • Abdus Salam,
  • Tariq O. Abbas,
  • Mansura Naznine,
  • Muhammad E. H. Chowdhury

摘要

Renal cortical abnormalities frequently signify severe kidney conditions, rendering their precise diagnosis crucial for clinical management and treatment strategy formulation. Nonetheless, manual evaluation of nuclear renal imaging is arduous and prone to considerable inter-observer variability, resulting in conflicting results. This paper presents a fully automated method for the differential detection of renal cortical anomalies via deep learning-based segmentation and classification. Initially, we generated an innovative compilation of rigorously annotated renal nuclear images from 613 patients. Among them 193 patients are primarily diagnosed with kidney scar. Utilizing this dataset, we devised a novel segmentation method to precisely identify and outline renal areas. The proposed DenseNet121_Self-ONN_FPN model combines the DenseNet121 backbone, Self-Organizing Neural Network (Self-ONN) layers in the Feature Pyramid Network (FPN) for enhanced performance in segmentation tasks achieving impressive results: an Accuracy of 98.74%, Intersection over Union (IoU) of 86.47%, Dice Similarity Coefficient (DSC) of 92.74%, precision of 92.61%, recall of 92.88%, F1-score of 99.29%, False Negative Rate (FNR) of 7.12%, and False Positive Rate (FPR) of 0.71%. We optimized a modified DenseNet205 model for the classification of renal cortical anomalies. We employed Contrast Limited Adaptive Histogram Equalization (CLAHE) and Gamma correction as a pre-processing measure to enhance image contrast and model efficacy. The model attained exceptional results, exceeding state-of-the-art techniques with an accuracy of 96.91%, precision of 96.98%, sensitivity of 96.91%, F1-score of 96.86%, and specificity of 95.87%. Furthermore, we used ScoreCAM explainable AI to produce heatmaps for the classification network, offering critical insights into the model’s decision-making process and guaranteeing transparency for clinical application. This automated pipeline overcomes the constraints of manual image analysis by improving precision, efficiency, and reliability, while establishing a new standard for renal segmentation and classification.