Dual-Intended Deep Learning Model for Breast Cancer Diagnosis in Ultrasound Imaging
1
Fischell Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
2
Department of Radiation Oncology, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, USA
3
Department of Computer Science, Perception Robotics and Intelligent Machines (PRIME) Research Group, University of Moncton, New Brunswick, NB E1A 3E9, Canada
4
Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada
5
Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
*
Author to whom correspondence should be addressed.
Cancers 2022, 14(11), 2663; https://doi.org/10.3390/cancers14112663
Received: 2 May 2022
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Revised: 21 May 2022
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Accepted: 24 May 2022
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Published: 27 May 2022
(This article belongs to the Special Issue Medical Imaging and Machine Learning)
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.
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.
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Keywords:
ultrasound imaging; breast cancer; medical image analysis; dimensionality reduction; deep learning; radiomics
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MDPI and ACS Style
Vigil, N.; Barry, M.; Amini, A.; Akhloufi, M.; Maldague, X.P.V.; Ma, L.; Ren, L.; Yousefi, B. Dual-Intended Deep Learning Model for Breast Cancer Diagnosis in Ultrasound Imaging. Cancers 2022, 14, 2663. https://doi.org/10.3390/cancers14112663
AMA Style
Vigil N, Barry M, Amini A, Akhloufi M, Maldague XPV, Ma L, Ren L, Yousefi B. Dual-Intended Deep Learning Model for Breast Cancer Diagnosis in Ultrasound Imaging. Cancers. 2022; 14(11):2663. https://doi.org/10.3390/cancers14112663
Chicago/Turabian StyleVigil, Nicolle, Madeline Barry, Arya Amini, Moulay Akhloufi, Xavier P.V. Maldague, Lan Ma, Lei Ren, and Bardia Yousefi. 2022. "Dual-Intended Deep Learning Model for Breast Cancer Diagnosis in Ultrasound Imaging" Cancers 14, no. 11: 2663. https://doi.org/10.3390/cancers14112663
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