Chronic Kidney Disease is a progressive medical ailment of growing global health importance because, in most cases, this ailment shows no symptoms during its early stages. Improving patients’ outcomes and early detection are significant aspects of managing diseases. In this paper, deep learning models to classify the images of kidney diseases are presented based on a dataset of 12, 446 images collected from various renal diseases. Therefore, CNN, VGG16, MobileNet V2, DenseNet 121, and ResNet 50 were the fine-tuned and evaluated models. The training setting was the Adam optimizer, categorical cross entropy loss, and 10 epochs. Hence, the model's performance was measured using the accuracy, precision, recall, and F1-score evaluation parameters. Following that, the current evaluation illustrates that most of the examined models positively predict outstanding accuracies, with ResNet 50 having a maximal validation and test accuracy rate reaching 99.40%. At the same time, MobileNet V2 and DenseNet 121 also boast of their high efficacy. The researchers' works highlighted that deep learning algorithms are very helpful for diagnosing kidney diseases based on medical images, underlining that their application can significantly change early diagnosis and patient treatment.

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