Published on: May 2026
CNN-BASED APPROACH FOR EARLY DIAGNOSIS OF KIDNEY DISEASE
G.Sharath Kumar K. Arjun C.Amar
Dr. Ksrk Sarma
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Abstract
Furthermore, the non-invasive nature of modern imaging modalities minimizes patient discomfort and lowers the risks associated with traditional invasive diagnostic procedures. The integration of Convolutional Neural Networks (CNNs) and machine learning algorithms enhances image analysis by improving feature extraction, classification accuracy, and diagnostic reliability. These automated systems assist clinicians in detecting subtle pathological changes that may be overlooked in manual assessments.
In addition, the ability to continuously monitor disease progression and evaluate treatment effectiveness through imaging data allows for dynamic adjustment of therapeutic strategies. This supports the development of personalized treatment plans tailored to individual patient needs. The adoption of such advanced imaging and AI-driven techniques has the potential to reduce diagnostic time, optimize clinical decision-making, and lower overall healthcare costs[1]
How to Cite this Paper
Kumar, G., Arjun, K. & C.Amar, (2026). CNN-Based Approach for Early Diagnosis of Kidney Disease. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(05). https://doi.org/10.55041/ijcope.v2i5.027
Kumar, G.Sharath, et al.. "CNN-Based Approach for Early Diagnosis of Kidney Disease." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i5.027.
Kumar, G.Sharath,K. Arjun, and C.Amar. "CNN-Based Approach for Early Diagnosis of Kidney Disease." International Journal of Creative and Open Research in Engineering and Management 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i5.027.
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- •Published on: May 15 2026
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