Kidney abnormalities, including cysts, tumors, and stones, affect millions worldwide and present substantial health challenges. Accurate detection and classification of these issues are essential but remain a difficult task. Computed Tomography (CT) scans, a specialized x-ray imaging technology, play a vital role in diagnosing these abnormalities. Recently, machine learning algorithms have become more prominent in these types of tasks; however, conventional machine learning techniques often struggle with data pre-processing, computational efficiency, and disease localization. To address these challenges, we have proposed an enhanced architecture of the YOLOv5 model. In this work, we primarily focused on designing a novel C3SE network method, which integrates Squeeze-and-Excitation (SE) modules with C3 (3Conv) modules within the YOLOv5, significantly improving the network’s ability to detect and classify kidney disorders. Additionally, to improve adaptive optimization of feature aggregation and enhanced detection accuracy, we have employed the BiFPN. Moreover, we incorporate the SE attention mechanism to enhance multi-scale feature extraction, effectively reducing the impact of undesired input features. To validate the performance of the proposed model compared to prior approaches, we evaluated its accuracy, mean average precision (mAP@0.5), precision, F1 score, and recall, achieving 97.5%, 96.8%, 94.1%, 97%, and 96%, respectively. To ensure model interpretability and diagnostic trust, we employed Grad-CAM based explainability, visually confirming that the model accurately localizes pathological features. This promotes transparency in AI-assisted diagnosis, crucial for clinical deployment.