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@article{179979,
        author = {Kashish Sinha and Priyanshu Shrivastava and Dr.Muthu Kumaran AMJ},
        title = {Nephromind - Detection ,classification and segmentation of chronic kidney disease},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {9113-9122},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179979},
        abstract = {Kidney disease is a serious global health threat 
that needs to be detected early and classified accurately 
for proper management. 
NephroMind is a deep-learning integrated system that is 
intended to automatically recognize, classify, and 
segment kidney issues based on medical imaging. 
Advanced CNNs and transformer-based models are 
employed in the technique to extract salient features from 
renal ultrasonography and MRI scans. With the 
integration of multiple classifiers, a hybrid ensemble 
learning scheme enhances diagnostic accuracy and 
robustness. 
Utilizing U-Net and attention techniques for detecting 
trouble areas, the segmentation module enhances 
interpretability. The reliability of the system across a 
broad spectrum of situations is guaranteed through 
rigorous testing on benchmark datasets. Feature selection 
is optimized using Principal Component Analysis (PCA) 
to maintain diagnostic accuracy while minimizing 
processing load. NephroMind outperforms current 
models when compared on accuracy, recall, and F1
score. This research contributes to the development of AI
supported nephrology by presenting an efficient, scalable, 
and explainable approach for kidney disease detection.},
        keywords = {},
        month = {May},
        }