Quantum Deep Learning for Automatic Chronic Kidney Disease Identification and Classification with CT images

Abstract

Chronic Kidney Disease (CKD) entails a progressive decline in renal function, often originating from comorbidities such as diabetes and hypertension. Early detection is crucial for managing progression and associated complications. Meanwhile, computed tomography (CT) serves as a vital tool for identifying kidney conditions. However, the accuracy and efficiency of radiological diagnoses are hampered by image quality, especially in early-stage disease. Despite extensive research on artificial intelligence (AI) based diagnostic models, high efficiency and optimal accuracy remains challenging. This study introduces a deep learning (DL) clinical diagnostic system aimed at enhancing the automatic identification and classification of CKD. Through an exploration of standard, advanced, and quantum DL algorithms in the CKD domain, it was selecting a hybrid quantum deep convolutional neural network (QDCNN) as our proposed model due to its high-quality performance. The model was optimized and refined through training with augmented and denoised datasets. This refined iteration yields impressive test performances in terms of accuracy: 99.98%, recall: 99.89%, precision: 99.84%, F1 score: 99.86%, specificity: 99.84%, Micro AUC: 99.99%, and testing time of 0.0641 seconds per image. Positioned to outperform existing methods, our proposed system demonstrates the potential to accurately identify kidney conditions at every stage, providing invaluable assistance to medical professionals and equipping them with an advanced level of accuracy, promptness, and reliability in diagnoses that is unparalleled in its excellence.