Application of Quantitative Interpretability to Evaluate CNN-Based Models for Medical Image Classification

Application of Quantitative Interpretability to Evaluate CNN-Based Models for Medical Image Classification

Convolutional Neural Networks (CNNs) dominate medical image classification, yet their “black box” nature limits understanding of their decision-making process. This study applies quantitative interpretability metrics to evaluate CNN performance in stained tongue coating recogni...

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Bibliographic Details
Main Authors: Nuan Cui, Yingjie Wu, Guojiang Xin, Jiaze Wu, Liqin Zhong, Hao Liang
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Explainable artificial intelligence
class activation mapping
tongue diagnosis
stained tongue coating
image classification
Online Access:https://ieeexplore.ieee.org/document/10990263/
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https://ieeexplore.ieee.org/document/10990263/

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