Image-based classification of kidney diseases leveraging Inceptionv3 and CNN

This study investigates the use of deep learning techniques, specifically Convolutional Neural Networks (CNNs) and the InceptionV3 architecture, to classify kidney diseases from CT images. Accurate and timely diagnosis of kidney disorders is vital for effective patient management, yet traditional methods can be time-consuming and require specialized expertise. Our research utilized a diverse dataset containing various kidney conditions, including normal, cysts, tumors, and stones, which we preprocessed and augmented to enhance model performance. The InceptionV3 architecture was selected for its ability to capture intricate patterns in images through its multi-scale processing capabilities. We trained the models using categorical cross-entropy loss and evaluated their performance based on accuracy, precision, and recall. The results demonstrated a significant level of accuracy in differentiating between the various kidney diseases, showcasing the potential of leveraging deep learning for improved diagnostic efficiency. This study not only highlights the promise of AI in medical imaging but also emphasizes the need for integrating advanced technologies in clinical practice to enhance patient outcomes. Future work will aim to further refine these models and validate them with larger datasets, ensuring their robustness and reliability in real-world applications.