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Numerical Interpretation of Variation Indices of Tumor Outline Tracking Using Artificial Intelligence Mapping.

Hamidreza Mortazavy Beni1

  • 1Department of Biomedical Engineering, Ars.C., Islamic Azad University, Arsanjan, Iran.

Biomedical Engineering and Computational Biology
|April 20, 2026
PubMed
Summary

Analyzing breast tumor contours with artificial intelligence (AI) significantly improves malignancy detection. This AI-driven approach offers a high-performance, low-complexity tool for enhanced breast cancer screening and diagnosis.

Keywords:
AI mappingensemble deep learningmedical image analysistumor outline trackingvariation indices

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Oncology
  • Machine Learning for Diagnostics

Background:

  • Accurate differentiation between benign and malignant breast tumors is crucial for effective patient management.
  • Traditional mammography analysis often uses whole-image processing, potentially overlooking critical tumor boundary information.
  • Tumor contour morphology offers valuable cues for malignancy assessment, enhancing diagnostic specificity and interpretability through AI.

Purpose of the Study:

  • To evaluate the diagnostic potential of deep learning-extracted tumor outline features from mammographic images.
  • To numerically and biologically interpret variations in these contour features for malignancy assessment.
  • To investigate the efficacy of combining deep features (ensemble approach) for improved classification accuracy.

Main Methods:

  • Analysis of a public dataset comprising 100 mammography tumor contours.
  • Feature extraction using eight deep learning models (e.g., ResNet50, Xception65, VGG16) and a feature-level ensemble.
  • Classification of extracted features using five machine learning algorithms (SVM, KNN, DT, Naive Bayes, shallow neural network).

Main Results:

  • The Xception65 model combined with Naive Bayes achieved 97.97% accuracy.
  • A feature ensemble with an ensemble classifier reached 96.96% accuracy, 95.45% sensitivity, and 98.48% specificity.
  • Naive Bayes demonstrated superior performance in integrating deep contour features across classifiers.

Conclusions:

  • Tumor contour analysis using AI provides biologically relevant malignancy indicators (irregularity, spiculation, shape complexity) independent of pixel intensity.
  • Outline-driven AI analysis offers a high-performance, low-complexity tool to enhance breast cancer screening.
  • Integration into clinical workflows can aid radiologists, potentially reducing false positives in mammographic diagnosis.