Survey on Machine Learning and Deep Learning Applications in Breast Cancer Diagnosis


Cancer is a fatal disease caused due to the undesirable spread of cells. Breast carcinoma is the most invasive tumors and is the main reason for cancer deaths in females. Therefore, early diagnosis and prognosis have become necessary to increase survivability and reduce death rates in the long run. New artificial intelligence technologies are assisting radiologists in medical image scrutiny, thereby improving cancer patients’ status. This survey enrolls peer-reviewed, newly developed computer-aided diagnosis (CAD) systems implementing machine learning (ML) and deep learning (DL) techniques for diagnosing breast carcinoma, compares them with previously established methods, and provides technical details with the pros and cons for each model. We also discuss some open issues, research gaps, and future research directions for the advanced CAD models in medical image analysis. Over the past decade, machine learning and deep learning have emerged as a subfield of artificial intelligence (AI), whose healthcare industry applications have provided excellent results with reduced cost and improved efficiency. This survey analyzes different classifiers of machine learning and deep learning approaches for breast cancer diagnosis. Results from previous studies proved that deep learning outperforms conventional machine learning for diagnosing breast carcinoma when the dataset is broad. Research gaps from the recent studies depict that practical and scientific research is an urgent necessity for improving healthcare in the long run.

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Artificial intelligence

ML :

Machine learning

DL :

Deep learning


Computer-aided diagnosis


Artificial neural network


Convolutional neural network


Deep convolutional neural network


Screen film mammography


Full-field digital mammography


Digital breast tomosynthesis


Magnetic resonance imaging






University of California Irvine


Wisconsin Breast Cancer Dataset


Mammography Image Analysis Society


Digital Database for Screening Mammography


Principal component analysis


Support vector machine


K-nearest neighbor


Logistic regression


Radial basis function


Naïve Bayes


Decision tree


Graphical processing unit


Least absolute shrinkage and a selection operator


Region of interest


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Correspondence to Shailender Kumar.

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Chugh, G., Kumar, S. & Singh, N. Survey on Machine Learning and Deep Learning Applications in Breast Cancer Diagnosis. Cogn Comput (2021).

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  • Breast cancer
  • Machine learning
  • Deep learning
  • Convolution neural network (CNN)
  • Computer-aided diagnosis (CAD)