Enhanced Feature Extraction for Detection and Classification of Kidney Abnormalities

Kidney abnormalities such as cysts, stones, tumors, and other structural disorders pose significant health risks and can lead to chronic kidney disease if not diagnosed in time.

This study proposes a deep learning-based diagnostic framework that introduces an enhanced feature extraction strategy through a novel model known as Kidney Transformer Network (KTNET). The system is designed to automatically detect and classify multiple kidney conditions by effectively extracting disease-specific features from CT scan images. By leveraging transformer-based architecture, KTNET improves feature representation and enables highly accurate discrimination between Normal, Cyst, Tumor, and Stone cases.

Experimental results demonstrate that the proposed model achieves outstanding diagnostic performance, recording 99.7% accuracy, 99.4% precision, 99.3% recall, and a 99.6% F1-score, surpassing traditional image processing methods and several existing deep learning models.

The model's adaptability and efficiency with diverse CT scan images highlight its potential for practical integration in clinical workflows.

This research advances medical imaging by providing an intelligent, reliable, and accurate framework for the early detection and classification of kidney abnormalities, ultimately enhancing patient diagnosis and clinical decision-making.