Abstract — Detecting kidney damage is an important step in the diagnosis and treatment of kidney disease. Deep learning methods have shown the potential to improve the accuracy and efficiency of kidney injury detection. Using medical imaging modalities such as ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and X-rays, we discuss recent research on the application of algorithms to deep learning to diagnosing kidney damage. We investigate the effectiveness of various deep learning architectures and algorithms, and the issues and limitations associated with using deep learning to identify kidney damage. Our study demonstrates that diagnosis of kidney damage using multiple imaging methods can be performed with high accuracy and efficiency using deep learning algorithms. However, there are a number of important limitations to overcome, including the need for large labelled data sets and the potential for bias. We also describe future goals of deep learning research for detecting kidney damage, such as creating understandable deep learning models and combining deep learning with other clinical data. Overall, this study highlights the potential of deep learning algorithms for better recognition of renal lesions and their contribution to improving the diagnosis and treatment of renal diseases.
Keywords— Kidney Lesion Detection, Deep Learning, Medical Imaging, Magnetic Resonance Imaging, Computed Tomography, Convolutional Neural Network, Segmentation.
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DOI:
10.17148/IJARCCE.2023.12579
