Dual-Intended Deep Learning Model for Breast Cancer Diagnosis in Ultrasound Imaging

SIMPLE SUMMARY: The findings of predictive and diagnostic systems in cancer are an intriguing topic for physicians and the oncologic community. Computer-aided decision (CAD) is vital for breast cancer diagnosis. It aids in higher accuracy and early, reliable diagnosis. To achieve such aims, diverse imaging modalities have been used and decision-making was facilitated by artificial intelligence and machine learning models. High-fidelity automated breast lesion finding, along with their corresponding radiomic feature biomarkers, can be delivered by a trained model. In this study, the potential impact of a machine learning model for detecting breast lesions and various radiomic biomarkers are examined. This study presents a model that automatically segments and extracts radiomics and can enable the clinical practice to find breast lesions while performing diagnosis concurrently. ABSTRACT: Automated medical data analysis demonstrated a significant role in modern medicine, and cancer diagnosis/prognosis to achieve highly reliable and generalizable systems. In this study, an automated breast cancer screening method in ultrasound imaging is proposed. A convolutional deep autoencoder model is presented for simultaneous segmentation and radiomic extraction. The model segments the breast lesions while concurrently extracting radiomic features. With our deep model, we perform breast lesion segmentation, which is linked to low-dimensional deep-radiomic extraction (four features). Similarly, we used high dimensional conventional imaging throughputs and applied spectral embedding techniques to reduce its size from 354 to 12 radiomics. A total of 780 ultrasound images—437 benign, 210, malignant, and 133 normal—were used to train and validate the models in this study. To diagnose malignant lesions, we have performed training, hyperparameter tuning, cross-validation, and testing with a random forest model. This resulted in a binary classification accuracy of 78.5% (65.1–84.1%) for the maximal (full multivariate) cross-validated model for a combination of radiomic groups.