Zahraa Ch. Oleiwi1,*, Zena H. Khalil1, Salwa Shakir Baawi1, Tara Sabah Mahdi2
1Department of Computer Information System, College of computer science and Information Technology, University of Al-Qadisiyah, Diwanya, Iraq
2College of Medicine, University of Al-Qadisiyah, Diwanya, Iraq.
*Corresponding Author: zahraa.chaffat@qu.edu.iq
Received 11 Nov. 2025, Accepted 1 Dec. 2025, published 30 Dec. 2025.
AbstractKey wordsDOI
Gastrointestinal (GI) endoscopic examinations can detect various GI issues early. Challenges like high intra-class variability, moderate differences between classes, and biased data complicate automated classification. The study analyzes frequency-dependent image features in classification results, focusing on similarities within and between classes to better understand the dataset and identify the most difficult classes to classify. It uses a similar approach for similarity analysis with Discrete Wavelet Transform (DWT), breaking images into low-frequency (LL) and high-frequency (HH) sub-bands based on frequency ranges. Structural Similarity Index Measure (SSIM) and Mean Squared Error (MSE) inter- and intra- class similarities. Functional validation involved a classification test using the Random Forest (RF) model. Experiments on multiple GI endoscopic datasets illustrate that LL sub-bands, capturing coarse structural features, provide higher discriminative power and improve classification accuracy, while HH sub-bands, preserving fine textures, are less effective due to higher inter-class similarity. Analysis of similarity measures highlights classes with high intra-class variability, particularly minority classes, as the most challenging for classification. The frequency-aware similarity approach enhances interpretability, reveals dataset-specific issues, and automates the evaluation of gastrointestinal images.
Gastrointestinal Endoscopic Image, Intra-Class Similarities, Discrete Wavelet Transform.
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