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. 2023 Jan 17;13(3):346.
doi: 10.3390/diagnostics13030346.

Development of an Artificial Intelligence-Based Breast Cancer Detection Model by Combining Mammograms and Medical Health Records

Nguyen Thi Hoang Trang  1 , Khuong Quynh Long  2 , Pham Le An  3   4 , Tran Ngoc Dang  4   5
Affiliations

Development of an Artificial Intelligence-Based Breast Cancer Detection Model by Combining Mammograms and Medical Health Records

Nguyen Thi Hoang Trang et al. Diagnostics (Basel). .

Abstract

Background: Artificial intelligence (AI)-based computational models that analyze breast cancer have been developed for decades. The present study was implemented to investigate the accuracy and efficiency of combined mammography images and clinical records for breast cancer detection using machine learning and deep learning classifiers.

Methods: This study was verified using 731 images from 357 women who underwent at least one mammogram and had clinical records for at least six months before mammography. The model was trained on mammograms and clinical variables to discriminate benign and malignant lesions. Multiple pre-trained deep CNN models to detect cancer in mammograms, including X-ception, VGG16, ResNet-v2, ResNet50, and CNN3 were employed. Machine learning models were constructed using k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), Artificial Neural Network (ANN), and gradient boosting machine (GBM) in the clinical dataset.

Results: The detection performance obtained an accuracy of 84.5% with a specificity of 78.1% at a sensitivity of 89.7% and an AUC of 0.88. When trained on mammography image data alone, the result achieved a slightly lower score than the combined model (accuracy, 72.5% vs. 84.5%, respectively).

Conclusions: A breast cancer-detection model combining machine learning and deep learning models was performed in this study with a satisfactory result, and this model has potential clinical applications.

Keywords: CNN; Resnet-v2; Resnet50; VGG16; Xception; artificial neural network; breast cancer; gradient boosting machine; k-nearest neighbor; random forest; support vector machine.

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Conflict of interest statement

The authors declare that there are no conflict of interest regarding this paper.

Figures

Figure 1
Figure 1
The flowchart of the proposed algorithm using ML-DL model for breast cancer detection.
Figure 2
Figure 2
The flowchart of model training, parameter tuning, and performance evaluation.
Figure 3
Figure 3
ROC curves of comparison of the classification performances.
Figure 4
Figure 4
The performance following the number of variables obtained from the RF-RFE method.
Figure 5
Figure 5
ROC curve of combination model performance.
See this image and copyright information in PMC

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