A Systematic Review on Breast Cancer Detection Using Deep Learning Techniques

Rautela, Kamakshi; Kumar, Dinesh; Kumar, Vijay

doi:10.1007/s11831-022-09744-5

Cited by 33 publications

(18 citation statements)

References 109 publications

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“…However, traditional computer-aided design systems generally depend on manually created features and therefore weaken the overall performance [13]. With the advent of machine learning and AI methods, deep learning-based techniques were recently studied for breast cancer detection [14,15]. Representation learning is the basis of deep learning techniques, which come from several layers.…”

Section: Introductionmentioning

confidence: 99%

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

Nasser

Yusof

2023

Diagnostics

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Breast cancer is one of the precarious conditions that affect women, and a substantive cure has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep learning techniques have been used effectively in breast cancer detection, facilitating early diagnosis and therefore increasing the chances of patients’ survival. Compared to classical machine learning techniques, deep learning requires less human intervention for similar feature extraction. This study presents a systematic literature review on the deep learning-based methods for breast cancer detection that can guide practitioners and researchers in understanding the challenges and new trends in the field. Particularly, different deep learning-based methods for breast cancer detection are investigated, focusing on the genomics and histopathological imaging data. The study specifically adopts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), which offer a detailed analysis and synthesis of the published articles. Several studies were searched and gathered, and after the eligibility screening and quality evaluation, 98 articles were identified. The results of the review indicated that the Convolutional Neural Network (CNN) is the most accurate and extensively used model for breast cancer detection, and the accuracy metrics are the most popular method used for performance evaluation. Moreover, datasets utilized for breast cancer detection and the evaluation metrics are also studied. Finally, the challenges and future research direction in breast cancer detection based on deep learning models are also investigated to help researchers and practitioners acquire in-depth knowledge of and insight into the area.

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Section: Introductionmentioning

confidence: 99%

“…Oyelade et al [23] examined different deep learning-based methods for breast cancer diagnosis from digital mammography. Husaini et al [15] examined the application of ML techniques and thermography for detecting breast cancer problems. In this method, various ML methods were investigated to process the breast cancer thermographic images.…”

Section: Introductionmentioning

confidence: 99%

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

Nasser

Yusof

2023

Diagnostics

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“…The main contributions of this review are as follows: Our paper provides a detailed review on the available breast cancer datasets of each of the 4 different modalities not available in past reviews [ 16 , 17 , 18 ]. We explore the methods in which recent deep learning algorithms are used to detect breast cancer using different types of screening.…”

Section: Introductionmentioning

confidence: 99%

“…Our paper provides a detailed review on the available breast cancer datasets of each of the 4 different modalities not available in past reviews [ 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Applying Deep Learning for Breast Cancer Detection in Radiology

Mahoro

Akhloufi

2022

Current Oncology

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Recent advances in deep learning have enhanced medical imaging research. Breast cancer is the most prevalent cancer among women, and many applications have been developed to improve its early detection. The purpose of this review is to examine how various deep learning methods can be applied to breast cancer screening workflows. We summarize deep learning methods, data availability and different screening methods for breast cancer including mammography, thermography, ultrasound and magnetic resonance imaging. In this review, we will explore deep learning in diagnostic breast imaging and describe the literature review. As a conclusion, we discuss some of the limitations and opportunities of integrating artificial intelligence into breast cancer clinical practice.

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“…Mammography is a reasonably quick procedure that is commonly available but it suffers from poor dynamic range, poor contrast, and a smudgy image. 11,12 Nonetheless, mammography is still widely used for screening breast cancer. 13 Ultrasound can be a more convenient option than mammography.…”

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confidence: 99%

Active contour and texture features hybrid model for breast cancer detection from ultrasonic images

Rautela

Kumar

2023

Int J Imaging Syst Tech

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Breast ultrasound is commonly used in the early detection of breast cancer. The existing geodesic‐based methods use pre‐defined filters that necessitate extensive prior knowledge to achieve the region of interest in input image. Furthermore, the majority of ultrasound images suffer from noise and acoustic shadowing, which reduces the accuracy of tumor detection. To make the breast ultrasound image more informative, the discriminative features can also be extracted to improve detection accuracy. This article proposes a method to combine Active Contour and Texture Feature Vectors to find discriminative patterns. A comprehensive set of discriminative features for cancer detection in ultrasound images is created by combining the two learning models. The Breast Ultrasound Images dataset is used to evaluate the suggested method and compare it to recently created algorithms. Experimental results reveal that the proposed approach outperforms the existing algorithms in terms of accuracy, recall, precision, Jaccard index, and F1 score.

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A Systematic Review on Breast Cancer Detection Using Deep Learning Techniques

Cited by 33 publications

References 109 publications

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

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

Applying Deep Learning for Breast Cancer Detection in Radiology

Active contour and texture features hybrid model for breast cancer detection from ultrasonic images

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