Predictor Model for Chronic Kidney Disease using Adaptive Gradient Clipping with Deep Neural Nets

This research aims to develop computer vision based predictive model for the three prominent kidney ailments namely Cyst, Stone, and Tumor which are common renal disorders that require timely medical intervention. This classification model is tested and trained using the multi-class CT Kidney Dataset which contains 12,446 images collected from PACS (Picture Archiving and Communication System) from different hospitals in Dhaka, Bangladesh. Initial models are build using plain VGG16, ResNet50, and InceptionV3 deep neural nets. Then after clip value filter of ADAM optimizer is applied which results in marginally improved accuracy and at the last Adaptive Gradient Clipping is applied as a replacement of batch norm process and this produces overall best results. The Adaptive Gradient Clipping based model achieves accuracy of 97.15% in VGG16, 99.5% in ResNet50, and 99.23% in InceptionV3. Overall classification metrics are best for ResNet50 and Inception V3 with Adaptive Gradient Clipping technique.

@article{Sharma2024,
  title     = {Predictor Model for Chronic Kidney Disease using Adaptive Gradient Clipping with Deep Neural Nets},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {4},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Neeraj Sharma and Praveen Lalwani},
  doi       = {10.14569/IJACSA.2024.01504119},
  url       = {https://doi.org/10.14569/IJACSA.2024.01504119}
}