Enhanced Feature Extraction for Detection and Classification of Kidney Abnormalities
Year: 2025
Type: article
Abstract: INTRODUCTION: 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. MATERIALS AND METHODS: 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. RESULTS: 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. DISCUSSION: The model's adaptability and efficiency with diverse CT scan images highlight its potential for practical integration in clinical workflows. CONCLUSION: 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.
Authors: Romail Khan, Rabbia Mahum, Usama Irshad, Mohammad Shehab, Faisal Shafique Butt
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4416906248