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

Mohan Bhandari, Pratheepan Yogarajah, Kavitha Subash Muthu, Joan Condell

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)
171 Downloads (Pure)

Abstract

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.
Original languageEnglish
Article number3125
Pages (from-to)1-17
Number of pages17
JournalApplied Sciences
Volume13
Issue number5
Early online date28 Feb 2023
DOIs
Publication statusPublished online - 28 Feb 2023

Bibliographical note

Funding Information:
We would like to thank Suman Parajuli and Aawish Bhandari for their invaluable guidance and support throughout the course of this research. Their expertise and insights were essential to the success of our study, and we are grateful for their time and efforts. Suman (MBBS/MD—Consultant Radiologist; Nepal Medical Council Number: 18206) from Pokhara Academy of Health Science Pokhara, and Aawish (MBBS—Medical Officer; Nepal Medical Council Number: 30071) from Gorkha Hospital, Nepal, have six and one years of experience in the related field, respectively. Their contributions are highly appreciated.

Publisher Copyright:
© 2023 by the authors.

Keywords

  • convolutional neural network
  • deep learning
  • Explainable Artificial Intelligence
  • Light Weight Model
  • Kidney abnormalities
  • explainable artificial intelligence
  • lightweight model
  • kidney abnormalities

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