The early and accurate diagnosis of kidney disease has several benefits of reducing the risk of its further progression and effective clinical management. Computed tomography (CT) imaging is a widely utilized approach by clinical experts for kidney stone detection (KSD). However, stone detection through CT images is a medically significant task as well as technically a challenging problem. In recent decades, deep learning (DL) models have occupied a distinctive place in handling such image data. However, these models are too complex in terms of number of layers and parameters, thus increases the computational cost. To overcome these limitations, this study suggests a custom six layer light weight sequential convolutional neural network (LSCNN) model for KSD with reduced computational requirements. The performance of the proposed model is compared with ten popular pre-trained models for detecting kidney stones from axial CT images by experimenting on a publicly available axial CT Imaging dataset. Experimental results conducted on 3364 CT images demonstrate that with a compact size of 10.61 MB, the proposed LSCNN model achieves superior performance, recording a classification accuracy of 96.7% and F-score of 96.6% surpassing the pre-trained models. These findings indicate that LSCNN is a light weight yet powerful alternative in resource-constrained scenarios, for kidney stone detection in CT images.
Dash et al. (2026) studied this question.