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Sahu A, Das PK, Meher S. Recent advancements in machine learning and deep learning-based breast cancer detection using mammograms. Phys Med 2023; 114:103138. [PMID: 37914431 DOI: 10.1016/j.ejmp.2023.103138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 07/22/2023] [Accepted: 09/14/2023] [Indexed: 11/03/2023] Open
Abstract
OBJECTIVE Mammogram-based automatic breast cancer detection has a primary role in accurate cancer diagnosis and treatment planning to save valuable lives. Mammography is one basic yet efficient test for screening breast cancer. Very few comprehensive surveys have been presented to briefly analyze methods for detecting breast cancer with mammograms. In this article, our objective is to give an overview of recent advancements in machine learning (ML) and deep learning (DL)-based breast cancer detection systems. METHODS We give a structured framework to categorize mammogram-based breast cancer detection techniques. Several publicly available mammogram databases and different performance measures are also mentioned. RESULTS After deliberate investigation, we find most of the works classify breast tumors either as normal-abnormal or malignant-benign rather than classifying them into three classes. Furthermore, DL-based features are more significant than hand-crafted features. However, transfer learning is preferred over others as it yields better performance in small datasets, unlike classical DL techniques. SIGNIFICANCE AND CONCLUSION In this article, we have made an attempt to give recent advancements in artificial intelligence (AI)-based breast cancer detection systems. Furthermore, a number of challenging issues and possible research directions are mentioned, which will help researchers in further scopes of research in this field.
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Affiliation(s)
- Adyasha Sahu
- Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela, Odisha, 769008, India.
| | - Pradeep Kumar Das
- School of Electronics Engineering (SENSE), VIT Vellore, Tamil Nadu, 632014, India.
| | - Sukadev Meher
- Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela, Odisha, 769008, India.
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Jalloul R, Chethan HK, Alkhatib R. A Review of Machine Learning Techniques for the Classification and Detection of Breast Cancer from Medical Images. Diagnostics (Basel) 2023; 13:2460. [PMID: 37510204 PMCID: PMC10378151 DOI: 10.3390/diagnostics13142460] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 07/17/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
Cancer is an incurable disease based on unregulated cell division. Breast cancer is the most prevalent cancer in women worldwide, and early detection can lower death rates. Medical images can be used to find important information for locating and diagnosing breast cancer. The best information for identifying and diagnosing breast cancer comes from medical pictures. This paper reviews the history of the discipline and examines how deep learning and machine learning are applied to detect breast cancer. The classification of breast cancer, using several medical imaging modalities, is covered in this paper. Numerous medical imaging modalities' classification systems for tumors, non-tumors, and dense masses are thoroughly explained. The differences between various medical image types are initially examined using a variety of study datasets. Following that, numerous machine learning and deep learning methods exist for diagnosing and classifying breast cancer. Finally, this review addressed the challenges of categorization and detection and the best results of different approaches.
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Affiliation(s)
- Reem Jalloul
- Maharaja Research Foundation, University of Mysore, Mysuru 570005, India
| | - H K Chethan
- Department of Computer Science and Engineering, Maharaja Research Foundation, Maharaja Institute of Technology, Mysuru 570004, India
| | - Ramez Alkhatib
- Biomaterial Bank Nord, Research Center Borstel Leibniz Lung Center, Parkallee 35, 23845 Borstel, Germany
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Alnowami MR, Abolaban FA, Taha E. A wrapper-based feature selection approach to investigate potential biomarkers for early detection of breast cancer. JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2022. [DOI: 10.1016/j.jrras.2022.01.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Aggarwal R, Sounderajah V, Martin G, Ting DSW, Karthikesalingam A, King D, Ashrafian H, Darzi A. Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ Digit Med 2021; 4:65. [PMID: 33828217 PMCID: PMC8027892 DOI: 10.1038/s41746-021-00438-z] [Citation(s) in RCA: 229] [Impact Index Per Article: 76.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 02/25/2021] [Indexed: 12/19/2022] Open
Abstract
Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify pathology in medical imaging. Searches were conducted in Medline and EMBASE up to January 2020. We identified 11,921 studies, of which 503 were included in the systematic review. Eighty-two studies in ophthalmology, 82 in breast disease and 115 in respiratory disease were included for meta-analysis. Two hundred twenty-four studies in other specialities were included for qualitative review. Peer-reviewed studies that reported on the diagnostic accuracy of DL algorithms to identify pathology using medical imaging were included. Primary outcomes were measures of diagnostic accuracy, study design and reporting standards in the literature. Estimates were pooled using random-effects meta-analysis. In ophthalmology, AUC's ranged between 0.933 and 1 for diagnosing diabetic retinopathy, age-related macular degeneration and glaucoma on retinal fundus photographs and optical coherence tomography. In respiratory imaging, AUC's ranged between 0.864 and 0.937 for diagnosing lung nodules or lung cancer on chest X-ray or CT scan. For breast imaging, AUC's ranged between 0.868 and 0.909 for diagnosing breast cancer on mammogram, ultrasound, MRI and digital breast tomosynthesis. Heterogeneity was high between studies and extensive variation in methodology, terminology and outcome measures was noted. This can lead to an overestimation of the diagnostic accuracy of DL algorithms on medical imaging. There is an immediate need for the development of artificial intelligence-specific EQUATOR guidelines, particularly STARD, in order to provide guidance around key issues in this field.
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Affiliation(s)
- Ravi Aggarwal
- Institute of Global Health Innovation, Imperial College London, London, UK
| | | | - Guy Martin
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Daniel S W Ting
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | | | - Dominic King
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Hutan Ashrafian
- Institute of Global Health Innovation, Imperial College London, London, UK.
| | - Ara Darzi
- Institute of Global Health Innovation, Imperial College London, London, UK
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Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10228298] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010–January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies.
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Jebamony J, Jacob D. Classification of Benign and Malignant Breast Masses on Mammograms for Large Datasets using Core Vector Machines. Curr Med Imaging 2020; 16:703-710. [PMID: 32723242 DOI: 10.2174/1573405615666190801121506] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2018] [Revised: 03/25/2019] [Accepted: 04/09/2019] [Indexed: 12/12/2022]
Abstract
BACKGROUND Breast cancer is one of the most leading causes of cancer deaths among women. Early detection of cancer increases the survival rate of the affected women. Machine learning approaches that are used for classification of breast cancer usually takes a lot of processing time during the training process. This paper attempts to propose a Machine Learning approach for breast cancer detection in mammograms, which does not depend on the number of training samples. OBJECTIVES The paper aims to develop a core vector machine-based diagnosis system for breast cancer detection using the date from MIAS. The main motivation behind using this system is to reduce the computational and memory requirement for large training data and to improve the classification accuracy. METHODS The proposed method has four stages: 1) Pre-processing is done to extract the breast region using global thresholding and enhancement using histogram equalization; 2) identification of potential mass using Otsu thresholding; 3) feature extraction using Laws Texture energy measures; and 4) mass detection is done using Core vector machine (CVM) classifier. RESULTS Comparative analysis was done with different existing algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Fuzzy Support Vector Machines (FSVM). The results illustrate that the proposed Core Vector Machine (CVM) classifier produced a promising result in terms of sensitivity (96.9%), misclassification rate (0.0443) and accuracy (95.89%). The time taken for training process is 0.0443, which is less when compared with other machine learning algorithms. CONCLUSION Performance analysis shows that CVM classifier is superior to other classifiers like ANN, SVM and FSVM. The computational time of the CVM classifier during the training process was also analysed and found to be better than other discussed algorithms. The results achieved show that CVM classifier is the best algorithm for breast mass detection in mammograms.
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Affiliation(s)
| | - Dheeba Jacob
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore Campus, Katpadi, India
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Jiang N, Zhong L, Zhang C, Luo X, Zhong P, Li X. Value of Conventional MRI Texture Analysis in the Differential Diagnosis of Phyllodes Tumors and Fibroadenomas of the Breast. Breast Care (Basel) 2020; 16:283-290. [PMID: 34248470 DOI: 10.1159/000508456] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 05/06/2020] [Indexed: 01/30/2023] Open
Abstract
Background There is substantial overlap in MRI findings between phyllodes tumors (PTs) and fibroadenomas (FAs). Our study was performed to investigate the value of conventional MRI texture analysis in the differential diagnosis of PTs and FAs. Methods Preoperative MRI data - including axial T1WI, T2WIFS (T2WI with fat suppression), dynamic contrast-enhanced (DCE)-T1WI2min and DCE-T1WI7min (T1WI post-strengthened for 2 and 7 min, respectively, on DCE-MRI) - of 45 patients with PTs and 67 patients with FAs were retrospectively analyzed. MaZda 4.7 software was used to manually draw the maximum ROIs at the same lesion level of the above MRI images. The optimized feature selection methods included Fisher's coefficient, probability of classification error and average correction coefficient (POE + ACC), and mutual information (MI) as well as a combination of the above 3 methods (F + POE + ACC + MI [FPM]), respectively. The misclassification rates of PTs and FAs were compared between texture analysis and subjective diagnosis by radiologists. Results The DCE-T1WI7min images had the lowest misclassification rate of 10.71% (12/112). The misclassification rate for the radiologists' analysis (31.25%, 35/112) was higher than that of all the texture analysis, and there was a statistically significant difference between the radiologists' misclassification rates and those from the FPM method in terms of the T2WIFS and DCE-T1WI2min images (all p < 0.05), and for the DCE-T1WI7min images by using the Fisher and FPM methods (all p < 0.05). Conclusion Texture analysis of conventional MRI can be used as an assistant tool in providing a certain objective basis for differentiating PTs from FAs.
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Affiliation(s)
- Nianping Jiang
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Li Zhong
- Department of Radiation Oncology, Daping Hospital, Army Medical University, Chongqing, China
| | - Chunlai Zhang
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Xiangguo Luo
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Peng Zhong
- Department of Pathology, Daping Hospital, Army Medical University, Chongqing, China
| | - Xiaoguang Li
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China
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Arora R, Rai PK, Raman B. Deep feature-based automatic classification of mammograms. Med Biol Eng Comput 2020; 58:1199-1211. [PMID: 32200453 DOI: 10.1007/s11517-020-02150-8] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 02/19/2020] [Indexed: 01/25/2023]
Abstract
Breast cancer has the second highest frequency of death rate among women worldwide. Early-stage prevention becomes complex due to reasons unknown. However, some typical signatures like masses and micro-calcifications upon investigating mammograms can help diagnose women better. Manual diagnosis is a hard task the radiologists carry out frequently. For their assistance, many computer-aided diagnosis (CADx) approaches have been developed. To improve upon the state of the art, we proposed a deep ensemble transfer learning and neural network classifier for automatic feature extraction and classification. In computer-assisted mammography, deep learning-based architectures are generally not trained on mammogram images directly. Instead, the images are pre-processed beforehand, and then they are adopted to be given as input to the ensemble model proposed. The robust features extracted from the ensemble model are optimized into a feature vector which are further classified using the neural network (nntraintool). The network was trained and tested to separate out benign and malignant tumors, thus achieving an accuracy of 0.88 with an area under curve (AUC) of 0.88. The attained results show that the proposed methodology is a promising and robust CADx system for breast cancer classification. Graphical Abstract Flow diagram of the proposed approach. Figure depicts the deep ensemble extracting the robust features with the final classification using neural networks.
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Affiliation(s)
- Ridhi Arora
- Indian Institute of Technology Roorkee, Roorkee, India.
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Xu Q, Wang X, Jiang H. Convolutional neural network for breast cancer diagnosis using diffuse optical tomography. Vis Comput Ind Biomed Art 2019; 2:1. [PMID: 32240400 PMCID: PMC7099566 DOI: 10.1186/s42492-019-0012-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 04/11/2019] [Indexed: 01/05/2023] Open
Abstract
We have developed a computer-aided diagnosis system based on a convolutional neural network that aims to classify breast mass lesions in optical tomographic images obtained using a diffuse optical tomography system, which is suitable for repeated measurements in mass screening. Sixty-three optical tomographic images were collected from women with dense breasts, and a dataset of 1260 2D gray scale images sliced from these 3D images was built. After image preprocessing and normalization, we tested the network on this dataset and obtained 0.80 specificity, 0.95 sensitivity, 90.2% accuracy, and 0.94 area under the receiver operating characteristic curve (AUC). Furthermore, a data augmentation method was implemented to alleviate the imbalance between benign and malignant samples in the dataset. The sensitivity, specificity, accuracy, and AUC of the classification on the augmented dataset were 0.88, 0.96, 93.3%, and 0.95, respectively.
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Affiliation(s)
- Qiwen Xu
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
- Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Xin Wang
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
- Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Huabei Jiang
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Department of Medical Engineering, University of South Florida, Tampa, FL, 33620, USA.
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Chougrad H, Zouaki H, Alheyane O. Deep Convolutional Neural Networks for breast cancer screening. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 157:19-30. [PMID: 29477427 DOI: 10.1016/j.cmpb.2018.01.011] [Citation(s) in RCA: 156] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Revised: 12/24/2017] [Accepted: 01/10/2018] [Indexed: 05/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Radiologists often have a hard time classifying mammography mass lesions which leads to unnecessary breast biopsies to remove suspicions and this ends up adding exorbitant expenses to an already burdened patient and health care system. METHODS In this paper we developed a Computer-aided Diagnosis (CAD) system based on deep Convolutional Neural Networks (CNN) that aims to help the radiologist classify mammography mass lesions. Deep learning usually requires large datasets to train networks of a certain depth from scratch. Transfer learning is an effective method to deal with relatively small datasets as in the case of medical images, although it can be tricky as we can easily start overfitting. RESULTS In this work, we explore the importance of transfer learning and we experimentally determine the best fine-tuning strategy to adopt when training a CNN model. We were able to successfully fine-tune some of the recent, most powerful CNNs and achieved better results compared to other state-of-the-art methods which classified the same public datasets. For instance we achieved 97.35% accuracy and 0.98 AUC on the DDSM database, 95.50% accuracy and 0.97 AUC on the INbreast database and 96.67% accuracy and 0.96 AUC on the BCDR database. Furthermore, after pre-processing and normalizing all the extracted Regions of Interest (ROIs) from the full mammograms, we merged all the datasets to build one large set of images and used it to fine-tune our CNNs. The CNN model which achieved the best results, a 98.94% accuracy, was used as a baseline to build the Breast Cancer Screening Framework. To evaluate the proposed CAD system and its efficiency to classify new images, we tested it on an independent database (MIAS) and got 98.23% accuracy and 0.99 AUC. CONCLUSION The results obtained demonstrate that the proposed framework is performant and can indeed be used to predict if the mass lesions are benign or malignant.
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Affiliation(s)
- Hiba Chougrad
- Laboratory of Computer Science and Mathematics and their Applications (LIMA), Faculty of science, University Chouaib Doukkali, El Jadida 24000, Morocco.
| | - Hamid Zouaki
- Laboratory of Computer Science and Mathematics and their Applications (LIMA), Faculty of science, University Chouaib Doukkali, El Jadida 24000, Morocco
| | - Omar Alheyane
- Laboratory of Fundamental Mathematics (LMF), Faculty of science, University Chouaib Doukkali, El Jadida 24000, Morocco
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Yassin NIR, Omran S, El Houby EMF, Allam H. Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 156:25-45. [PMID: 29428074 DOI: 10.1016/j.cmpb.2017.12.012] [Citation(s) in RCA: 120] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Revised: 11/26/2017] [Accepted: 12/11/2017] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE The high incidence of breast cancer in women has increased significantly in the recent years. Physician experience of diagnosing and detecting breast cancer can be assisted by using some computerized features extraction and classification algorithms. This paper presents the conduction and results of a systematic review (SR) that aims to investigate the state of the art regarding the computer aided diagnosis/detection (CAD) systems for breast cancer. METHODS The SR was conducted using a comprehensive selection of scientific databases as reference sources, allowing access to diverse publications in the field. The scientific databases used are Springer Link (SL), Science Direct (SD), IEEE Xplore Digital Library, and PubMed. Inclusion and exclusion criteria were defined and applied to each retrieved work to select those of interest. From 320 studies retrieved, 154 studies were included. However, the scope of this research is limited to scientific and academic works and excludes commercial interests. RESULTS This survey provides a general analysis of the current status of CAD systems according to the used image modalities and the machine learning based classifiers. Potential research studies have been discussed to create a more objective and efficient CAD systems.
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Affiliation(s)
- Nisreen I R Yassin
- Systems & Information Department, Engineering Research Division, National Research Centre, Dokki, Cairo 12311, Egypt.
| | - Shaimaa Omran
- Systems & Information Department, Engineering Research Division, National Research Centre, Dokki, Cairo 12311, Egypt.
| | - Enas M F El Houby
- Systems & Information Department, Engineering Research Division, National Research Centre, Dokki, Cairo 12311, Egypt.
| | - Hemat Allam
- Anaesthesia & Pain, Medical Division, National Research Centre, Dokki, Cairo 12311, Egypt.
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Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network. INFORMATION 2018. [DOI: 10.3390/info9010019] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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Involvement of Machine Learning for Breast Cancer Image Classification: A Survey. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2017; 2017:3781951. [PMID: 29463985 PMCID: PMC5804413 DOI: 10.1155/2017/3781951] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Accepted: 10/26/2017] [Indexed: 11/17/2022]
Abstract
Breast cancer is one of the largest causes of women's death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors' and physicians' time. Despite the various publications on breast image classification, very few review papers are available which provide a detailed description of breast cancer image classification techniques, feature extraction and selection procedures, classification measuring parameterizations, and image classification findings. We have put a special emphasis on the Convolutional Neural Network (CNN) method for breast image classification. Along with the CNN method we have also described the involvement of the conventional Neural Network (NN), Logic Based classifiers such as the Random Forest (RF) algorithm, Support Vector Machines (SVM), Bayesian methods, and a few of the semisupervised and unsupervised methods which have been used for breast image classification.
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