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<p>In this study, we introduce FlexiLPQ, an innovative image classification model developed as the feature-engineering counterpart of FlexiViT, and evaluate its performance on Osteoid Osteoma diagnosis. For this purpose, a new Osteoid Osteoma CT dataset was curated. Using this dataset, our goal was to design an automatic detection system capable of identifying Osteoid Osteoma with high accuracy.</p><p>The proposed FlexiLPQ model operates through five main phases: (i) multi-patch feature extraction using local phase quantization (LPQ), (ii) feature selection via cumulative weighted iterative neighborhood component analysis (CWINCA), (iii) classification using a t-algorithm–based k-nearest neighbors (tkNN) classifier, (iv) iterative majority voting (IMV) to refine the decision, and (v) selection of the best final outcome.</p><p>FlexiLPQ was applied to the curated CT dataset and achieved a 98.89% classification accuracy. Additionally, multiple patch sizes were incorporated, and their performance differences were analyzed. The results clearly show that FlexiLPQ is an effective and robust image classification framework, and it is well suited for biomedical imaging tasks such as Osteoid Osteoma detection.</p>

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FlexiLPQ: automated osteoid osteoma detection using computed tomography

  • 期刊论文

  • 发表日期：

    2026年1月14日

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  • Sefa Key,
  • Anil Agar,
  • Ilknur Sercek,
  • Ahmet Kursad Poyraz,
  • Mehmet Baygin,
  • Sengul Dogan,
  • Turker Tuncer

摘要

In this study, we introduce FlexiLPQ, an innovative image classification model developed as the feature-engineering counterpart of FlexiViT, and evaluate its performance on Osteoid Osteoma diagnosis. For this purpose, a new Osteoid Osteoma CT dataset was curated. Using this dataset, our goal was to design an automatic detection system capable of identifying Osteoid Osteoma with high accuracy.

The proposed FlexiLPQ model operates through five main phases: (i) multi-patch feature extraction using local phase quantization (LPQ), (ii) feature selection via cumulative weighted iterative neighborhood component analysis (CWINCA), (iii) classification using a t-algorithm–based k-nearest neighbors (tkNN) classifier, (iv) iterative majority voting (IMV) to refine the decision, and (v) selection of the best final outcome.

FlexiLPQ was applied to the curated CT dataset and achieved a 98.89% classification accuracy. Additionally, multiple patch sizes were incorporated, and their performance differences were analyzed. The results clearly show that FlexiLPQ is an effective and robust image classification framework, and it is well suited for biomedical imaging tasks such as Osteoid Osteoma detection.