Hybrid Deep Learning Framework for Classification of Kidney CT Images: Diagnosis of Stones, Cysts, and Tumors

TL;DR

This paper presents a hybrid deep learning model combining ResNet101 and a custom CNN for accurate classification of kidney CT images into four categories, achieving near-perfect accuracy and promising clinical utility.

Contribution

The study introduces a novel hybrid CNN architecture that fuses features from ResNet101 and a custom CNN, outperforming standalone models in kidney disease classification.

Findings

Achieved 99.73% training accuracy and 100% testing accuracy.

Outperformed standalone ResNet101 in classification tasks.

Reduced testing time and improved diagnostic precision.

Abstract

Medical image classification is a vital research area that utilizes advanced computational techniques to improve disease diagnosis and treatment planning. Deep learning models, especially Convolutional Neural Networks (CNNs), have transformed this field by providing automated and precise analysis of complex medical images. This study introduces a hybrid deep learning model that integrates a pre-trained ResNet101 with a custom CNN to classify kidney CT images into four categories: normal, stone, cyst, and tumor. The proposed model leverages feature fusion to enhance classification accuracy, achieving 99.73% training accuracy and 100% testing accuracy. Using a dataset of 12,446 CT images and advanced feature mapping techniques, the hybrid CNN model outperforms standalone ResNet101. This architecture delivers a robust and efficient solution for automated kidney disease diagnosis, providing improved precision, recall, and reduced testing time, making it highly suitable for clinical applications.