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Diagnostics
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Machine Learning in Breast Cancer Diagnosis and Prognosis
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Machine Learning in Breast Cancer Diagnosis and Prognosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 18284

Special Issue Editor

Dr. Ryad Zemouri

E-Mail Website
Guest Editor
Centre de Recherche d’Hydro-Québec (CHRQ), Dir. Recherche et Innovation - Production, 1800, Boul. Lionel-Boulet, Varennes, Quebec City, QC J3X 1S1, Canada
Interests: hydro-generator diagnosis and prognosis; deep learning and artificial intelligence; signal and image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Breast cancer is the most common cancer suffered by women in the world, with more than 2 million new cases in 2018, and represents one of the main public health problems. Decisions regarding chemotherapy for a patient with a particular profile of breast cancer disease are not easy to consider. This decision may be influenced by different factors and depends on the diagnosis procedure, which is based on a set of morphological or immunohistochemical features such as the tumor size, the nodal involvement, the histological grade and type, hormone receptor for estrogen (ER) and progesterone (PR) expression, the HER2 status and the Ki67 proliferation index.

In this Special Issue of Diagnostics, devoted to machine learning in breast cancer diagnosis and prognosis, we invite research and review articles that discuss 1) malignancy diagnosis, which deals with the benign/malignant classification of breast cancer to determine whether cancer is present or not; and 2) prognosis, which deals with malignancy grading, involves the classification of the malignancy stage and tries to determine the cancer risk level, such as by the prediction of the Oncotype-DX. Further results from studies regarding innovative machine learning techniques applied to the classification of breast cancer in histopathological images or clinical data affected by data imbalances are welcome. 

Prof. Dr. Ryad Zemouri
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Breast cancer
  • Machine learning
  • Deep learning
  • Unbalanced dataset
  • Histopathological images
  • Clinical data

Published Papers (3 papers)

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Research

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26 pages, 8678 KiB  
Article
Conventional Machine Learning versus Deep Learning for Magnification Dependent Histopathological Breast Cancer Image Classification: A Comparative Study with Visual Explanation
by Said Boumaraf, Xiabi Liu, Yuchai Wan, Zhongshu Zheng, Chokri Ferkous, Xiaohong Ma, Zhuo Li and Dalal Bardou
Diagnostics 2021, 11(3), 528; https://doi.org/10.3390/diagnostics11030528 - 16 Mar 2021
Cited by 50 | Viewed by 5177
Abstract
Breast cancer is a serious threat to women. Many machine learning-based computer-aided diagnosis (CAD) methods have been proposed for the early diagnosis of breast cancer based on histopathological images. Even though many such classification methods achieved high accuracy, many of them lack the [...] Read more.
Breast cancer is a serious threat to women. Many machine learning-based computer-aided diagnosis (CAD) methods have been proposed for the early diagnosis of breast cancer based on histopathological images. Even though many such classification methods achieved high accuracy, many of them lack the explanation of the classification process. In this paper, we compare the performance of conventional machine learning (CML) against deep learning (DL)-based methods. We also provide a visual interpretation for the task of classifying breast cancer in histopathological images. For CML-based methods, we extract a set of handcrafted features using three feature extractors and fuse them to get image representation that would act as an input to train five classical classifiers. For DL-based methods, we adopt the transfer learning approach to the well-known VGG-19 deep learning architecture, where its pre-trained version on the large scale ImageNet, is block-wise fine-tuned on histopathological images. The evaluation of the proposed methods is carried out on the publicly available BreaKHis dataset for the magnification dependent classification of benign and malignant breast cancer and their eight sub-classes, and a further validation on KIMIA Path960, a magnification-free histopathological dataset with 20 image classes, is also performed. After providing the classification results of CML and DL methods, and to better explain the difference in the classification performance, we visualize the learned features. For the DL-based method, we intuitively visualize the areas of interest of the best fine-tuned deep neural networks using attention maps to explain the decision-making process and improve the clinical interpretability of the proposed models. The visual explanation can inherently improve the pathologist’s trust in automated DL methods as a credible and trustworthy support tool for breast cancer diagnosis. The achieved results show that DL methods outperform CML approaches where we reached an accuracy between 94.05% and 98.13% for the binary classification and between 76.77% and 88.95% for the eight-class classification, while for DL approaches, the accuracies range from 85.65% to 89.32% for the binary classification and from 63.55% to 69.69% for the eight-class classification. Full article
(This article belongs to the Special Issue Machine Learning in Breast Cancer Diagnosis and Prognosis)
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19 pages, 2584 KiB  
Article
Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine
by Vivek Lahoura, Harpreet Singh, Ashutosh Aggarwal, Bhisham Sharma, Mazin Abed Mohammed, Robertas Damaševičius, Seifedine Kadry and Korhan Cengiz
Diagnostics 2021, 11(2), 241; https://doi.org/10.3390/diagnostics11020241 - 04 Feb 2021
Cited by 122 | Viewed by 6658
Abstract
Globally, breast cancer is one of the most significant causes of death among women. Early detection accompanied by prompt treatment can reduce the risk of death due to breast cancer. Currently, machine learning in cloud computing plays a pivotal role in disease diagnosis, [...] Read more.
Globally, breast cancer is one of the most significant causes of death among women. Early detection accompanied by prompt treatment can reduce the risk of death due to breast cancer. Currently, machine learning in cloud computing plays a pivotal role in disease diagnosis, but predominantly among the people living in remote areas where medical facilities are scarce. Diagnosis systems based on machine learning act as secondary readers and assist radiologists in the proper diagnosis of diseases, whereas cloud-based systems can support telehealth services and remote diagnostics. Techniques based on artificial neural networks (ANN) have attracted many researchers to explore their capability for disease diagnosis. Extreme learning machine (ELM) is one of the variants of ANN that has a huge potential for solving various classification problems. The framework proposed in this paper amalgamates three research domains: Firstly, ELM is applied for the diagnosis of breast cancer. Secondly, to eliminate insignificant features, the gain ratio feature selection method is employed. Lastly, a cloud computing-based system for remote diagnosis of breast cancer using ELM is proposed. The performance of the cloud-based ELM is compared with some state-of-the-art technologies for disease diagnosis. The results achieved on the Wisconsin Diagnostic Breast Cancer (WBCD) dataset indicate that the cloud-based ELM technique outperforms other results. The best performance results of ELM were found for both the standalone and cloud environments, which were compared. The important findings of the experimental results indicate that the accuracy achieved is 0.9868, the recall is 0.9130, the precision is 0.9054, and the F1-score is 0.8129. Full article
(This article belongs to the Special Issue Machine Learning in Breast Cancer Diagnosis and Prognosis)
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Review

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13 pages, 1988 KiB  
Review
The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review
by Tomoyuki Fujioka, Mio Mori, Kazunori Kubota, Jun Oyama, Emi Yamaga, Yuka Yashima, Leona Katsuta, Kyoko Nomura, Miyako Nara, Goshi Oda, Tsuyoshi Nakagawa, Yoshio Kitazume and Ukihide Tateishi
Diagnostics 2020, 10(12), 1055; https://doi.org/10.3390/diagnostics10121055 - 06 Dec 2020
Cited by 53 | Viewed by 5271
Abstract
Breast cancer is the most frequently diagnosed cancer in women; it poses a serious threat to women’s health. Thus, early detection and proper treatment can improve patient prognosis. Breast ultrasound is one of the most commonly used modalities for diagnosing and detecting breast [...] Read more.
Breast cancer is the most frequently diagnosed cancer in women; it poses a serious threat to women’s health. Thus, early detection and proper treatment can improve patient prognosis. Breast ultrasound is one of the most commonly used modalities for diagnosing and detecting breast cancer in clinical practice. Deep learning technology has made significant progress in data extraction and analysis for medical images in recent years. Therefore, the use of deep learning for breast ultrasonic imaging in clinical practice is extremely important, as it saves time, reduces radiologist fatigue, and compensates for a lack of experience and skills in some cases. This review article discusses the basic technical knowledge and algorithms of deep learning for breast ultrasound and the application of deep learning technology in image classification, object detection, segmentation, and image synthesis. Finally, we discuss the current issues and future perspectives of deep learning technology in breast ultrasound. Full article
(This article belongs to the Special Issue Machine Learning in Breast Cancer Diagnosis and Prognosis)
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