RT Journal Article
T1 A novel explainable AI framework for medical image classification integrating statistical, visual, and rule-based methods
A1 Ullah, Naeem
A1 Guzmán-Aroca, Florentina
A1 Martínez-Álvarez, Francisco
A1 De Falco, Ivanoe
A1 Sannino, Giovanna
K1 Explainable artificial intelligence
K1 Medical image classification
K1 Feature engineering
K1 Rule-based interpretability
AB Artificial intelligence and deep learning are powerful tools for extracting knowledge from large datasets, particularly in healthcare. However, their black-box nature raises interpretability concerns, especially in high-stakes applications. Existing eXplainable Artificial Intelligence methods often focus solely on visualization or rule-based explanations, limiting interpretability’s depth and clarity. This work proposes a novel explainable AI method specifically designed for medical image analysis, integrating statistical, visual, and rule-based explanations to improve transparency in deep learning models. Statistical features are derived from deep features extracted using a custom Mobilenetv2 model. A two-step feature selection method – zero-based filtering with mutual importance selection – ranks and refines these features. Decision tree and RuleFit models are employed to classify data and extract human-readable rules. Additionally, a novel statistical feature map overlay visualization generates heatmap-like representations of three key statistical measures (mean, skewness, and entropy), providing both localized and quantifiable visual explanations of model decisions. The proposed method has been validated on five medical imaging datasets – COVID-19 radiography, ultrasound breast cancer, brain tumor magnetic resonance imaging, lung and colon cancer histopathological, and glaucoma images – with results confirmed by medical experts, demonstrating its effectiveness in enhancing interpretability for medical image classification tasks.
PB Elsevier
YR 2025
FD 2025-06-06
LK https://hdl.handle.net/10433/25815
UL https://hdl.handle.net/10433/25815
LA en
NO Naeem Ullah, Florentina Guzmán-Aroca, Francisco Martínez-Álvarez, Ivanoe De Falco, Giovanna Sannino, A novel explainable AI framework for medical image classification integrating statistical, visual, and rule-based methods, Medical Image Analysis, Volume 105, 2025, 103665, ISSN 1361-8415, https://doi.org/10.1016/j.media.2025.103665
NO UPO
DS RIO
RD May 9, 2026