AI-DRIVEN RENAL IMAGE CLASSIFICATION FOR DETECTING KIDNEY ANOMALIES IDENTIFICATION OF CYSTS, STONES, TUMOR AND HEALTHY TISSUE
Keywords:
Renal image classification, deep learning, convolutional neural networks (CNNs), kidney abnormalities, automated diagnosis.Abstract
Medical imaging is vital for diagnosing and monitoring renal conditions such as cysts, stones, and tumors. However, traditional methods, primarily based on manual inspection by radiologists, can be time-consuming, subjective, and prone to human error. With the rise of deep learning and advanced image processing, there is increasing interest in automating renal image classification to enhance
diagnostic accuracy and consistency.
References
V. Jha, G. Garcia-Garcia, K. Iseki et al., “Chronic kidney disease: global dimension and perspectives,” The Lancet, vol. 382, no. 9888, pp. 260–272, 2013.
R. Ruiz-Arenas, “A summary of worldwide national activities in chronic kidney disease (CKD) testing, the electronic journal of the international federation of,” Clinical Chemistry and Laboratory Medicine, vol. 28, no. 4, pp. 302–314, 2017.
