Novel Deep Learning Method for Automated Diagnosis of Kidney Disease from Medical Image Using CNN

Kidney disease is a significant global health concern, often requiring early diagnosis for effective treatment and management. This study proposes a deep learning-based system for classifying kidney conditions from CT scan images into four categories: Normal, Cyst, Tumor, and Stone. The system leverages advanced image preprocessing techniques and a convolutional neural network (CNN) to achieve high accuracy in disease detection.The model is trained on a curated dataset of kidney CT scan images, using data augmentation and optimization techniques to enhance its performance and generalization. The system ensures efficient processing, achieving an average inference time of under 2 seconds per image, making it suitable for real-time applications. Rigorous testing demonstrates the model's robustness and reliability, even in scenarios with variations in image quality, noise, and lighting conditions. This automated diagnosis system has significant potential in diverse healthcare settings, including telemedicine, clinical diagnostics, and portable diagnostic devices. Its scalability and compatibility with resource-constrained environments further extend its usability to underserved regions. By providing fast, accurate, and reliable results, the proposed system aids radiologists in early detection, enhances diagnostic accuracy, and contributes to better patient outcomes