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Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction.

Maged Nasser¹, Umi Kalsom Yusof¹

¹School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia.

Diagnostics (Basel, Switzerland)

|January 8, 2023

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Deep learning, particularly Convolutional Neural Networks (CNNs), shows promise for accurate breast cancer detection using AI. This systematic review highlights CNNs as the leading method, guiding future research in AI-driven diagnostics.

Area of Science:

Oncology
Artificial Intelligence
Medical Imaging

Background:

Breast cancer remains a significant health concern for women globally, with ongoing research focused on improving early detection and treatment outcomes.
Artificial intelligence (AI), specifically deep learning, is emerging as a powerful tool in medical diagnostics, offering enhanced capabilities for identifying complex patterns in biological data.
Traditional machine learning methods often require extensive manual feature engineering, whereas deep learning models can automatically extract relevant features, streamlining the diagnostic process.

Purpose of the Study:

To conduct a systematic literature review of deep learning-based methods for breast cancer detection.
To analyze current trends, challenges, and future research directions in the application of deep learning to breast cancer diagnostics.
To provide a comprehensive overview for researchers and practitioners in the field, focusing on genomics and histopathological imaging data.

Keywords:

artificial neural network breast cancer breast cancer diagnosis cancer detection deep learning

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Main Methods:

A systematic literature review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
Searched and gathered relevant studies, followed by eligibility screening and quality evaluation.
Identified and analyzed 98 eligible articles focusing on deep learning techniques for breast cancer detection.

Main Results:

Convolutional Neural Networks (CNNs) were identified as the most accurate and widely adopted deep learning model for breast cancer detection.
Accuracy metrics are the predominant method for evaluating the performance of these deep learning models.
The review also examined datasets commonly used and evaluation metrics employed in breast cancer detection studies.

Conclusions:

Deep learning models, especially CNNs, offer significant advancements in the accuracy and efficiency of breast cancer detection.
The findings underscore the potential of AI to improve early diagnosis and patient survival rates.
Further research is needed to address existing challenges and explore new avenues in AI-driven breast cancer diagnostics.