Exploring Early Prediction of CKD Using InfNet Segmentation with Cross Domain-Based Few Shot Learning.

Chronic kidney disease (CKD) is a life-threatening condition which is hard to identify early, as there are no symptoms. It is possible to prevent or minimize the evolution of this chronic condition before it reaches an end stage, where dialysis or surgical intervention is the only way to save the patient’s life. Early detection and adequate therapy can increase the risk of this occurring. The majority of supervised techniques employ labeled datasets to build in-domain predictions of CKD. However, the results are typically poor when a classifier is used to categorize an image of CKD in a different domain after it has been trained on labeled images for that domain. However, a machine learning technique known as “cross-domain few-shot learning” involves training a model to generalize information from one domain to another using only a small number of samples from the target domain. So, a novel hybrid classifier with few shot learning is proposed to improve the cross-domain CKD prediction performance. The kidney’s CT images were collected and preprocessed using ENSNet annotation, autoencoder denoising, green fire blue filter and image restoration. The pre-data were provided for the segmentation process using supervised InfNet segmentation, which segments an exact region of CKD. The segmented regions were fed to an input of the classification process. A few shot learning-based hybrid classifiers were developed to predict CKD in the cross-domain. In a few shot learning, more samples were collected for the training phase, and fewer samples were used for the classifier’s testing. Based on the few shot learning, the proposed CKD model was tested and diagnosed with the input samples at appropriate conditions. The designed proposed model offers 97.9% accuracy, 96.4% precision, 96.4% recall and 98.6% negative predictive value. In addition, observed values of the proposed model were contrasted with some other approaches for validating the process. The proposed few-shot learning-based hybrid classifier is an effective choice for more precise cross-domain CKD prediction at low processing time. [ABSTRACT FROM AUTHOR]