A Systematic Literature Review of Breast Cancer Diagnosis Using Machine Intelligence Techniques

Nemade, Varsha; Pathak, Sunil; Dubey, Ashutosh Kumar

doi:10.1007/s11831-022-09738-3

Cited by 33 publications

(16 citation statements)

References 181 publications

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“…When a single classifier fails to perform as expected, an ensemble classifier may be used to provide a significant improvement over the individual classifier. It was found that ensemble classifiers are more resilient and sufficient for illness prediction than individual classifiers since they train several classifiers to anticipate the final prediction outcome [19][20][21][22][23][24]. As per the research, the ensemble classifiers outperformed the individual/single classifiers in a variety of other applications fields.…”

Section: Introductionmentioning

confidence: 78%

A review and methodological analysis of cardiovascular disease prediction and detection

2022

IJACR

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According to the world health organization (WHO) cardiovascular diseases (CVDs) are the leading cause of death globally. This paper examines and explores different methodological contribution in the detection and prediction of CVDs. This paper mainly covers the ways of applicability of the approaches, domain of applicability, results, advantages, challenges and limitations. It also investigated the datasets from different repository. The exploration clearly discusses the major challenges along with the suggestive measures. The results indicate the combination of approaches and it may varyaccording to the symptoms and its nature.

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Section: Introductionmentioning

confidence: 78%

A review and methodological analysis of cardiovascular disease prediction and detection

2022

IJACR

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“…In recent times, deep learning is a common tool used to detect breast cancer. Deep learning methods have been demonstrated to be capable of diagnosing breast cancer up to 12 months earlier than those using conventional clinical procedures [16]. In addition, the techniques can be used to learn the most pertinent features to best tackle the issue.…”

Section: Rq1: What Are the Common Deep Learning Methods For Breast Ca...mentioning

confidence: 99%

“…In contrast, the CNN techniques using previously trained neural networks such as AlexNet, residual neural network (ResNet), visual geometry group (VGG), etc. are called "transfer learning (TL)-based methods" [16]. Several methods used CNN-based methods for breast cancer diagnosis.…”

Section: Rq1: What Are the Common Deep Learning Methods For Breast Ca...mentioning

confidence: 99%

“…Others used the deep CNN method to classify and identify tumor-related stroma for breast cancer diagnosis [55][56][57][58]. CNN is the most popular deep-learning technique that has been utilized in several studies for breast cancer detection [16]. The CNN is a deep learning model which represents hierarchical abstraction and consists of various layers which accept features as raw data [24].…”

Section: Rq1: What Are the Common Deep Learning Methods For Breast Ca...mentioning

confidence: 99%

“…The representation is transformed from the lower to the higher levels at each end by combining the non-linear and simple modules, in which the lower-level features are more comprehensible and the higherlevel features more abstract [14]. Compared to the ML methods [10,16], deep learning is more effective and requires fewer human interventions for the related pattern recognition schemes. This makes it capable of effectively solving complex problems in various areas such as image analysis [17], pattern recognition and natural language processing.…”

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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Image‐Based Flow Regime Recognition in Aerated Stirred Tanks Using Deep Transfer Learning

Khaydarov

Becker

Urbas

2023

Chemie Ingenieur Technik

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Monitoring of flow regimes in aerated stirred tanks is important to ensure energy efficiency and product quality. The use of deep learning models for the recognition of flow regimes shows promising results. However, such models require a large amount of data for training. The aim of this paper is to apply the deep transfer learning approach to address this challenge. We compare various pre‐trained models with the differential learning rate and 2‐step transfer learning approaches to analyse the resultant model performance. We also investigate the effect of the dataset size on the classification accuracy.

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A Systematic Literature Review of Breast Cancer Diagnosis Using Machine Intelligence Techniques

Cited by 33 publications

References 181 publications

A review and methodological analysis of cardiovascular disease prediction and detection

A review and methodological analysis of cardiovascular disease prediction and detection

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

Image‐Based Flow Regime Recognition in Aerated Stirred Tanks Using Deep Transfer Learning

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