Automated Detection and Classification of Kidney Tumours in CT Images Using Deep Learning

ABSTRACT

Kidney tumours (KTs) constitute a significant global health burden, ranking as the fourteenth most prevalent tumour type among men and women worldwide. Early detection is critical for reducing mortality, enabling timely preventive measures, and improving treatment outcomes; however, traditional diagnostic methods are often time‐consuming, labour‐intensive and reliant on specialist interpretation. Deep learning (DL) based automated detection systems offer a promising alternative by improving accuracy, reducing costs and alleviating radiologists' workload. Despite extensive research, limitations such as inadequate datasets and sub‐optimal detection techniques have hindered progress. This study proposes a deep learning (DL) based framework for the automated detection and classification of KTs using computed tomography (CT) images. A dataset of 7770 images from 111 patients was preprocessed and augmented to enhance model generalization. For the task of tumour detection, six DL architectures were evaluated, with a custom CNN12 model achieving the highest performance (accuracy: 99.43%, precision: 0.99, recall: 1.00, 1‐score: 0.99), outperforming several baseline and transfer learning models. For the subsequent classification of detected tumours into benign and malignant types, a custom CNN11 model attained superior results (accuracy: 97.17%, precision: 0.95, recall: 0.97, 1‐score: 0.96), demonstrating robust classification capability. These results indicate a significant improvement over many existing approaches and validate the effectiveness of the proposed two‐phase framework. The findings demonstrate that the proposed framework can accurately distinguish KTs from normal tissues and further classify their malignancy with high precision. This approach has the potential to serve as a decision‐support tool in clinical settings, assisting radiologists by reducing diagnostic time, minimizing the risk of misdiagnosis and facilitating earlier intervention to improve patient outcomes.