The application of traditional machine learning and deep learning techniques in mammography: a review

Abstract

Breast cancer, the most prevalent malignant tumor among women, poses a significant threat to patients’ physical and mental well-being. Recent advances in early screening technology have facilitated the early detection of an increasing number of breast cancers, resulting in a substantial improvement in patients’ overall survival rates. The primary techniques used for early breast cancer diagnosis include mammography, breast ultrasound, breast MRI, and pathological examination. However, the clinical interpretation and analysis of the images produced by these technologies often involve significant labor costs and rely heavily on the expertise of clinicians, leading to inherent deviations. Consequently, artificial intelligence(AI) has emerged as a valuable technology in breast cancer diagnosis. Artificial intelligence includes Machine Learning(ML) and Deep Learning(DL). By simulating human behavior to learn from and process data, ML and DL aid in lesion localization reduce misdiagnosis rates, and improve accuracy. This narrative review provides a comprehensive review of the current research status of mammography using traditional ML and DL algorithms. It particularly highlights the latest advancements in DL methods for mammogram image analysis and offers insights into future development directions.

Hybrid Intelligent Pattern Recognition Systems for Mass Segmentation and Classification: A Pilot Study on Full-Field Digital Mammograms

“…The highest accuracy that they achieved was 99.58%. Gao et al [12] represented a review of deep and machine learning techniques for mammog-raphy, assuming that the most frequent supervised methods for classification are artificial neural networks (ANNs), support vector machine (SVM) and random forest (RF). The most frequent unsupervised methods are the clustering algorithms of K-means, principal component analysis (PCA) and singular value decomposition (SVD).…”

Breast cancer screening in women taking hormone replacement therapy needs updating

“…An RCT is not suited for rare events, and breast cancer trials require huge numbers taking time to perform with the risk of being outdated before being finished. This explains the lack of data on newer techniques in ultrasound, digital imaging, deep learning ( Arun Kumar and Sasikala, 2023 ) and artificial intelligence ( Gao et al, 2023 ). The translation of predictive values or Bayesian probabilities, into guidelines, results from estimating truth or clinical importance, decided by consensus or voting by a group of experts ( Kubota et al, 2023 ), thus introducing subjectivity based on previous experiences.…”

Largescale multicenter study of a serum metabolite biomarker panel for the diagnosis of breast cancer

“… 9 Additionally, interpretation and analysis of the images often rely heavily on the expertise of clinicians, leading to inherent deviations. 10 For noninvasive screening, carbohydrate antigen 15-3 (CA15-3) and carcinoembryonic antigen (CEA) are currently the most widely used serum clinical tumor markers. 11 However, both have low sensitivity, especially in early stage BC patients, increasing the urgency of finding more sensitive and noninvasive biomarkers for the screening and diagnosis of BC at the early stage.…”