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Numerical Interpretation of Variation Indices of Tumor Outline Tracking Using Artificial Intelligence Mapping
1Department of Biomedical Engineering, Ars.C., Islamic Azad University, Arsanjan, Iran.
Background:
Accurate differentiation between benign and malignant breast tumors is critical for early diagnosis and treatment planning. Traditional approaches often rely on whole-image processing; however, the tumor contour contains rich morphological cues that can independently support malignancy assessment. Leveraging these contour-based features using artificial intelligence (AI) can enhance diagnostic specificity and interpretability.
Objective:
This study aims to evaluate the diagnostic potential of tumor outline features extracted from mammographic images using deep learning models, with a focus on interpreting their variations numerically and biologically. It also investigates whether combining deep features (ensemble approach) can improve classification accuracy.
Methods:
A public dataset of 100 mammography tumor contours was analyzed. Eight deep learning models (ResNet50, Xception65, VGG16, AlexNet, DenseNet, GoogLeNet, Inception-v3, and a feature-level ensemble) were used for feature extraction. These features were then classified using 5 machine learning algorithms: SVM, KNN, DT, Naive Bayes, and a shallow neural network. Performance metrics included accuracy, sensitivity, specificity, and precision.
Results:
Xception65 with Naive Bayes achieved 97.97% accuracy, while the feature ensemble with an ensemble classifier achieved 96.96% accuracy, 95.45% sensitivity, and 98.48% specificity. Naive Bayes consistently outperformed other classifiers in integrating deep contour features.
Conclusion And Clinical Interpretation:
Tumor contour-based analysis provides biologically meaningful indicators of malignancy-such as irregularity, spiculation, and shape complexity-without relying on full pixel intensity. The results demonstrate that outline-driven AI analysis can enhance breast cancer screening by offering a low-complexity, high-performance diagnostic tool. Future integration into clinical workflows may aid radiologists in real-time and reduce false positives in mammographic diagnosis.
