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Diagnostics
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Application of Artificial Intelligence in Early Breast Cancer Detection
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Application of Artificial Intelligence in Early Breast Cancer Detection

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 April 2024) | Viewed by 18993

Special Issue Editor

Prof. Dr. Mohamed Hadi Habaebi

E-Mail Website
Guest Editor
Electrical and Computer Engineering Department, International Islamic University Malaysia, Kuala Lumpur, Malaysia
Interests: biomedical applications of AI

Special Issue Information

Dear Colleagues,

The goal of this Special Issue is to invite researchers from around the world to discuss the latest advances in early breast cancer detection and diagnosis using artificial intelligence and machine learning techniques. We are particularly interested in papers that focus on novel methods of early detection, such as imaging techniques and other similar innovative approaches. We also welcome papers that discuss the challenges associated with early diagnosis, such as cost-effectiveness, patient access, and ethical considerations.

We invite submissions from disciplines related to breast cancer research, including but not limited to the use of AI and machine learning in conjunction with medical imaging techniques such as thermography, mammography, CT, MRI, and ultrasound. Submissions should include original research findings or reviews of existing literature. All accepted papers will be published in the Diagnostics journal.

We look forward to receiving your submissions.

Prof. Dr. Mohamed Hadi Habaebi
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

  • artificial intelligence
  • machine learning
  • deep learning
  • thermography
  • mammogram
  • CT
  • MRI
  • ultrasound

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Published Papers (4 papers)

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Research

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16 pages, 3357 KiB  
Article
Histopathological Image Diagnosis for Breast Cancer Diagnosis Based on Deep Mutual Learning
by Amandeep Kaur, Chetna Kaushal, Jasjeet Kaur Sandhu, Robertas Damaševičius and Neetika Thakur
Diagnostics 2024, 14(1), 95; https://doi.org/10.3390/diagnostics14010095 - 31 Dec 2023
Cited by 8 | Viewed by 3176
Abstract
Every year, millions of women across the globe are diagnosed with breast cancer (BC), an illness that is both common and potentially fatal. To provide effective therapy and enhance patient outcomes, it is essential to make an accurate diagnosis as soon as possible. [...] Read more.
Every year, millions of women across the globe are diagnosed with breast cancer (BC), an illness that is both common and potentially fatal. To provide effective therapy and enhance patient outcomes, it is essential to make an accurate diagnosis as soon as possible. In recent years, deep-learning (DL) approaches have shown great effectiveness in a variety of medical imaging applications, including the processing of histopathological images. Using DL techniques, the objective of this study is to recover the detection of BC by merging qualitative and quantitative data. Using deep mutual learning (DML), the emphasis of this research was on BC. In addition, a wide variety of breast cancer imaging modalities were investigated to assess the distinction between aggressive and benign BC. Based on this, deep convolutional neural networks (DCNNs) have been established to assess histopathological images of BC. In terms of the Break His-200×, BACH, and PUIH datasets, the results of the trials indicate that the level of accuracy achieved by the DML model is 98.97%, 96.78, and 96.34, respectively. This indicates that the DML model outperforms and has the greatest value among the other methodologies. To be more specific, it improves the results of localization without compromising the performance of the classification, which is an indication of its increased utility. We intend to proceed with the development of the diagnostic model to make it more applicable to clinical settings. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Early Breast Cancer Detection)
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41 pages, 11213 KiB  
Article
Analyzing Histological Images Using Hybrid Techniques for Early Detection of Multi-Class Breast Cancer Based on Fusion Features of CNN and Handcrafted
by Mohammed Al-Jabbar, Mohammed Alshahrani, Ebrahim Mohammed Senan and Ibrahim Abdulrab Ahmed
Diagnostics 2023, 13(10), 1753; https://doi.org/10.3390/diagnostics13101753 - 17 May 2023
Cited by 9 | Viewed by 2187
Abstract
Breast cancer is the second most common type of cancer among women, and it can threaten women’s lives if it is not diagnosed early. There are many methods for detecting breast cancer, but they cannot distinguish between benign and malignant tumors. Therefore, a [...] Read more.
Breast cancer is the second most common type of cancer among women, and it can threaten women’s lives if it is not diagnosed early. There are many methods for detecting breast cancer, but they cannot distinguish between benign and malignant tumors. Therefore, a biopsy taken from the patient’s abnormal tissue is an effective way to distinguish between malignant and benign breast cancer tumors. There are many challenges facing pathologists and experts in diagnosing breast cancer, including the addition of some medical fluids of various colors, the direction of the sample, the small number of doctors and their differing opinions. Thus, artificial intelligence techniques solve these challenges and help clinicians resolve their diagnostic differences. In this study, three techniques, each with three systems, were developed to diagnose multi and binary classes of breast cancer datasets and distinguish between benign and malignant types with 40× and 400× factors. The first technique for diagnosing a breast cancer dataset is using an artificial neural network (ANN) with selected features from VGG-19 and ResNet-18. The second technique for diagnosing breast cancer dataset is by ANN with combined features for VGG-19 and ResNet-18 before and after principal component analysis (PCA). The third technique for analyzing breast cancer dataset is by ANN with hybrid features. The hybrid features are a hybrid between VGG-19 and handcrafted; and a hybrid between ResNet-18 and handcrafted. The handcrafted features are mixed features extracted using Fuzzy color histogram (FCH), local binary pattern (LBP), discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM) methods. With the multi classes data set, ANN with the hybrid features of the VGG-19 and handcrafted reached a precision of 95.86%, an accuracy of 97.3%, sensitivity of 96.75%, AUC of 99.37%, and specificity of 99.81% with images at magnification factor 400×. Whereas with the binary classes data set, ANN with the hybrid features of the VGG-19 and handcrafted reached a precision of 99.74%, an accuracy of 99.7%, sensitivity of 100%, AUC of 99.85%, and specificity of 100% with images at a magnification factor 400×. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Early Breast Cancer Detection)
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13 pages, 4495 KiB  
Article
Classification of Breast Lesions on DCE-MRI Data Using a Fine-Tuned MobileNet
by Long Wang, Ming Zhang, Guangyuan He, Dong Shen and Mingzhu Meng
Diagnostics 2023, 13(6), 1067; https://doi.org/10.3390/diagnostics13061067 - 11 Mar 2023
Cited by 5 | Viewed by 1766
Abstract
It is crucial to diagnose breast cancer early and accurately to optimize treatment. Presently, most deep learning models used for breast cancer detection cannot be used on mobile phones or low-power devices. This study intended to evaluate the capabilities of MobileNetV1 and MobileNetV2 [...] Read more.
It is crucial to diagnose breast cancer early and accurately to optimize treatment. Presently, most deep learning models used for breast cancer detection cannot be used on mobile phones or low-power devices. This study intended to evaluate the capabilities of MobileNetV1 and MobileNetV2 and their fine-tuned models to differentiate malignant lesions from benign lesions in breast dynamic contrast-enhanced magnetic resonance images (DCE-MRI). Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Early Breast Cancer Detection)
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Review

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36 pages, 3382 KiB  
Review
Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024
by Alessandro Carriero, Léon Groenhoff, Elizaveta Vologina, Paola Basile and Marco Albera
Diagnostics 2024, 14(8), 848; https://doi.org/10.3390/diagnostics14080848 - 19 Apr 2024
Cited by 7 | Viewed by 10876
Abstract
The rapid advancement of artificial intelligence (AI) has significantly impacted various aspects of healthcare, particularly in the medical imaging field. This review focuses on recent developments in the application of deep learning (DL) techniques to breast cancer imaging. DL models, a subset of [...] Read more.
The rapid advancement of artificial intelligence (AI) has significantly impacted various aspects of healthcare, particularly in the medical imaging field. This review focuses on recent developments in the application of deep learning (DL) techniques to breast cancer imaging. DL models, a subset of AI algorithms inspired by human brain architecture, have demonstrated remarkable success in analyzing complex medical images, enhancing diagnostic precision, and streamlining workflows. DL models have been applied to breast cancer diagnosis via mammography, ultrasonography, and magnetic resonance imaging. Furthermore, DL-based radiomic approaches may play a role in breast cancer risk assessment, prognosis prediction, and therapeutic response monitoring. Nevertheless, several challenges have limited the widespread adoption of AI techniques in clinical practice, emphasizing the importance of rigorous validation, interpretability, and technical considerations when implementing DL solutions. By examining fundamental concepts in DL techniques applied to medical imaging and synthesizing the latest advancements and trends, this narrative review aims to provide valuable and up-to-date insights for radiologists seeking to harness the power of AI in breast cancer care. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Early Breast Cancer Detection)
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