Advancements in Kidney Disease Diagnosis: The Role of Deep Learning Techniques in Modern Medicine

The detection of kidney diseases is essential for early clinical intervention and effective therapy. In this paper, a classification model is described for detecting four types of kidney conditions: normal, cyst, tumor, and stone. This model was applied by training a convolutional neural network on a diverse dataset to detect a high degree of accuracy and reliability. The performance metrics of the model for classification are evaluated in terms of precision, recall, and F1-score. For class Cyst, the model obtained a precision of 89.64%, a recall of 88.56%, and an F1-score of 89.10% with the support of 556 instances. The Normal class obtained metrics with an accuracy of 90.00%, a recall of 89.87%, and an F1-score of $\mathbf{8 9. 9 3 \%}$ across the support of 762 cases. Lastly, on the Stone class, the model added a precision of 86.26%, a recall of 89.52%, and an F1-score of 87.86% from the 207 instances. Tumor class: $\mathbf{9 0. 0 0 \%}$ precision, recall, and $F 1$-score for 342 instances. It achieved an accuracy of 90.46% on 1867 instances in total. Its macro average precision, recall, and F1-score were 88.97%, 89.49%, and 89.22%, respectively, while the weighted average precision, recall, and F1-score were $89.48 \%, 89.46 \%$, and $\mathbf{8 9. 4 7 \%}$. Most importantly, these results demonstrate that this proposed classification approach is very practical for diagnosis concerning kidney diseases and thus greatly aids the medical fraternity in clinical decision-making.