Enhancing Chronic Kidney Disease Diagnosis Through Densenet-121 Approach

This research work focuses on creating an advanced deep learning framework designed for the automated classification of kidney stones, cyst, normal, tumor a critical task in medical imaging with significant implications for diagnosis and treatment planning. The proposed system incorporates deep learning, specifically a pre-trained DenseNet121 model. To adapt this model to the intricacies of medical imaging data, this study introduces custom layers atop the pre-trained architecture. These layers are designed to extract and analyze features relevant to kidney stone classification, enabling the model to differentiate between four distinct classes: Cyst, Normal, Stone, and Tumor. Importantly, to ensure the robustness and generalization of the proposed model, advanced regularization techniques such as dropout and batch normalization are employed during training. Through extensive experimentation and evaluation on diverse datasets, this study demonstrates the efficacy and reliability of the proposed approach. The results highlight the system's capability to achieve high accuracy in classifying kidney stones, highlighting its capacity as a valuable resource for healthcare professionals in nephrology and urology. By facilitating accurate and efficient diagnosis, the proposed framework aims to contribute to improve clinical decision-making processes and ultimately enhance patient care in the domain of kidney stone management.