Year: 2025
Type: conference-paper
Abstract: Diabetic Nephropathy (DN), a common and severe complication of diabetes, can lead to chronic kidney disease and eventual kidney failure.Early and accurate detection is essential to enable timely medical intervention and improve patient outcomes.This study applies a deep learning approach using the VGG16 architecture to classify and detect DN from medical images.Initially pre-trained on large image datasets, the model was fine-tuned for kidney ultrasound and tissue image analysis to improve feature extraction and classification accuracy.Performance was assessed using standard evaluation metrics such as accuracy, precision, recall, F1-score, and AUC-ROC.The model demonstrated promising results, outperforming conventional methods in classification tasks.This work highlights the potential of deep learning as a non-invasive and reliable solution for early-stage DN detection, contributing to better diagnostic support in healthcare settings.
Authors: Vedant Ransing, Ranjeetsingh Suryawanshi, Manav Bhasin, Piyush Male, Atharva Raskar
Institution: Vishwakarma Institute of Technology
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4411094142