Renal Tubular Disorders

Pyeritz, Reed E.

doi:10.1016/b978-0-12-812534-2.00011-4

Cited by 1 publication

(2 citation statements)

References 62 publications

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“…Red pixels increased the likelihood of predicting a class once the Shapley values were converted into pixels, while blue pixels reduced the likelihood [58]. The Shapley values were generated by Equation (1).…”

Section: Cnn Modelmentioning

confidence: 99%

“…The kidney, an abdominal organ that filter waste and excess water from the blood, can developed abnormalities [1] that have a significant impact on health and affect over 10% of the worldwide population [2]. The exact causes and mechanisms of kidney abnormalities have yet to be fully understood [3], but research in recent years has identified a variety of genetic, environmental, and lifestyle factors that may contribute to their development [4].…”

Section: Introductionmentioning

confidence: 99%

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Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP

et al. 2023

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Kidney abnormality is one of the major concerns in modern society, and it affects millions of people around the world. To diagnose different abnormalities in human kidneys, a narrow-beam x-ray imaging procedure, computed tomography, is used, which creates cross-sectional slices of the kidneys. Several deep-learning models have been successfully applied to computer tomography images for classification and segmentation purposes. However, it has been difficult for clinicians to interpret the model’s specific decisions and, thus, creating a “black box” system. Additionally, it has been difficult to integrate complex deep-learning models for internet-of-medical-things devices due to demanding training parameters and memory-resource cost. To overcome these issues, this study proposed (1) a lightweight customized convolutional neural network to detect kidney cysts, stones, and tumors and (2) understandable AI Shapely values based on the Shapley additive explanation and predictive results based on the local interpretable model-agnostic explanations to illustrate the deep-learning model. The proposed CNN model performed better than other state-of-the-art methods and obtained an accuracy of 99.52 ± 0.84% for K = 10-fold of stratified sampling. With improved results and better interpretive power, the proposed work provides clinicians with conclusive and understandable results.

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Section: Cnn Modelmentioning

confidence: 99%