Kidney abnormalities—such as cysts, tumors, and stones—pose serious global health concerns and require accurate, timely diagnosis. While CT scans remain a critical diagnostic tool, improving detection efficiency and reliability through advanced AI models is increasingly important for better clinical outcomes.
Abstract:
Detecting and classifying kidney abnormalities using CT scans is challenging due to limitations in traditional machine learning approaches, including preprocessing inefficiencies and poor localization. To overcome this, we propose an enhanced YOLOv5 architecture featuring a novel C3SE module that combines Squeeze-and-Excitation (SE) with C3 layers for improved feature learning. The integration of BiFPN further optimizes feature aggregation and detection accuracy, while SE attention enhances multi-scale feature extraction by reducing noise. The model achieves strong performance, with 97.5% accuracy, 96.8% mAP@0.5, 94.1% precision, 97% F1 score, and 96% recall. Additionally, Grad-CAM-based explainability validates accurate localization of pathological features, supporting transparency and clinical applicability of the model.
Explanation in Layperson’s Terms:
This research focuses on developing an intelligent system that can automatically detect kidney diseases from CT images. Typically, doctors need to carefully examine these scans to identify conditions such as kidney stones, tumors, or cysts, which can be time-consuming and sometimes difficult, particularly when abnormalities are small or unclear. To address this, we designed a deep learning-based model that can quickly and accurately analyze medical images and identify different types of kidney abnormalities. The system not only detects the disease but also highlights the exact area in the image where the problem is located, helping doctors better understand and trust the results of the model. This approach supports early diagnosis, reduces the workload on healthcare professionals, and improves the chances of timely treatment, making it a valuable tool for enhancing medical decision-making in clinical practice.
Practical Implementation and Social Implication:
The practical implementation of this research lies in deploying the proposed C3SE-YOLO deep learning framework as a computer-aided diagnostic tool in clinical settings to assist radiologists in analyzing CT scan images for kidney abnormalities such as stones, tumors, and cysts. The model can be integrated into existing medical imaging systems to provide fast, accurate, and automated detection along with visual explanations using Grad-CAM, which highlights the affected regions and improves clinical trust and interpretability.
From a social perspective, this research supports early and reliable diagnosis of kidney diseases, helping in timely medical intervention and reducing the risk of severe health complications. It also reduces the workload on healthcare professionals and enhances the overall efficiency of diagnostic processes, contributing to improved patient care and better healthcare outcomes.
Future Research Plan:
Future research plans focus on further enhancing the proposed deep learning framework by extending it to 3D medical image analysis for improved segmentation and more precise localization of kidney abnormalities. It also aims to incorporate advanced attention mechanisms and multimodal data integration to improve diagnostic accuracy and robustness across different imaging conditions. In addition, a key direction of future work is to develop a lightweight and efficient version of the model for deployment on embedded edge devices, enabling real-time kidney disease diagnosis with low computational requirements. There is also a plan to design a clinician-friendly interface that integrates detection, visualization, and decision support, making the system more practical for real-world healthcare applications and improving its usability in clinical environments.
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