Breast cancer detection and classification with digital breast tomosynthesis: a two-stage deep learning approach.

Purpose: The purpose of this study was to propose a new computer-assisted two-staged diagnosis system that combines a modified deep learning (DL) architecture (VGG19) for the classification of digital breast tomosynthesis (DBT) images with the detection of tumors as benign or cancerous using the You Only Look Once version 5 (YOLOv5) model combined with the convolutional block attention module (CBAM) (known as YOLOv5-CBAM).

Methods: In the modified version of VGG19, eight additional layers were integrated, comprising four batch normalization layers and four additional pooling layers (two max pooling and two average pooling). The CBAM was incorporated into the YOLOv5 model structure after each feature fusion. The experiment was carried out using a sizable benchmark dataset of breast tomography images. A total of 22,032 DBT examinations from 5,060 patients were included in the data.

Results: Test accuracy, training loss, and training accuracy showed better performance with our proposed architecture than with previous models. Hence, the modified VGG19 classified DBT images more accurately than previously possible using pre-trained model-based architectures. Furthermore, a YOLOv5-based CBAM precisely discriminated between benign lesions and those that were malignant.

Conclusion: DBT images can be classified using modified VGG19 with accuracy greater than the previously available pre-trained models-based architectures. Furthermore, a YOLOv5-based CBAM can precisely distinguish between benign and cancerous lesions.

Clinical significance: The proposed two-tier DL algorithm, combining a modified VGG19 model for image classification and YOLOv5-CBAM for lesion detection, can improve the accuracy, efficiency, and reliability of breast cancer screening and diagnosis through innovative artificial intelligence-driven methodologies.