1
|
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.
Collapse
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.
| |
Collapse
|
2
|
Shaikh TA, Ali R. An automated machine learning tool for breast cancer diagnosis for healthcare professionals. Health Syst (Basingstoke) 2021; 11:303-333. [DOI: 10.1080/20476965.2021.1966324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
Affiliation(s)
- Tawseef Ayoub Shaikh
- Department Of Computer Science & Engineering, Baba Ghulam Shah Badshah University Rajouri, Rajouri, J&K, India
| | - Rashid Ali
- Department Of Computer Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
| |
Collapse
|
3
|
Breast Cancer Segmentation Methods: Current Status and Future Potentials. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9962109. [PMID: 34337066 PMCID: PMC8321730 DOI: 10.1155/2021/9962109] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 05/14/2021] [Accepted: 06/11/2021] [Indexed: 12/24/2022]
Abstract
Early breast cancer detection is one of the most important issues that need to be addressed worldwide as it can help increase the survival rate of patients. Mammograms have been used to detect breast cancer in the early stages; if detected in the early stages, it can drastically reduce treatment costs. The detection of tumours in the breast depends on segmentation techniques. Segmentation plays a significant role in image analysis and includes detection, feature extraction, classification, and treatment. Segmentation helps physicians quantify the volume of tissue in the breast for treatment planning. In this work, we have grouped segmentation methods into three groups: classical segmentation that includes region-, threshold-, and edge-based segmentation; machine learning segmentation; and supervised and unsupervised and deep learning segmentation. The findings of our study revealed that region-based segmentation is frequently used for classical methods, and the most frequently used techniques are region growing. Further, a median filter is a robust tool for removing noise. Moreover, the MIAS database is frequently used in classical segmentation methods. Meanwhile, in machine learning segmentation, unsupervised machine learning methods are more frequently used, and U-Net is frequently used for mammogram image segmentation because it does not require many annotated images compared with other deep learning models. Furthermore, reviewed papers revealed that it is possible to train a deep learning model without performing any preprocessing or postprocessing and also showed that the U-Net model is frequently used for mammogram segmentation. The U-Net model is frequently used because it does not require many annotated images and also because of the presence of high-performance GPU computing, which makes it easy to train networks with more layers. Additionally, we identified mammograms and utilised widely used databases, wherein 3 and 28 are public and private databases, respectively.
Collapse
|
4
|
Rehman KU, Li J, Pei Y, Yasin A, Ali S, Mahmood T. Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network. SENSORS 2021; 21:s21144854. [PMID: 34300597 PMCID: PMC8309805 DOI: 10.3390/s21144854] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/12/2021] [Accepted: 07/12/2021] [Indexed: 01/21/2023]
Abstract
Microcalcification clusters in mammograms are one of the major signs of breast cancer. However, the detection of microcalcifications from mammograms is a challenging task for radiologists due to their tiny size and scattered location inside a denser breast composition. Automatic CAD systems need to predict breast cancer at the early stages to support clinical work. The intercluster gap, noise between individual MCs, and individual object’s location can affect the classification performance, which may reduce the true-positive rate. In this study, we propose a computer-vision-based FC-DSCNN CAD system for the detection of microcalcification clusters from mammograms and classification into malignant and benign classes. The computer vision method automatically controls the noise and background color contrast and directly detects the MC object from mammograms, which increases the classification performance of the neural network. The breast cancer classification framework has four steps: image preprocessing and augmentation, RGB to grayscale channel transformation, microcalcification region segmentation, and MC ROI classification using FC-DSCNN to predict malignant and benign cases. The proposed method was evaluated on 3568 DDSM and 2885 PINUM mammogram images with automatic feature extraction, obtaining a score of 0.97 with a 2.35 and 0.99 true-positive ratio with 2.45 false positives per image, respectively. Experimental results demonstrated that the performance of the proposed method remains higher than the traditional and previous approaches.
Collapse
Affiliation(s)
- Khalil ur Rehman
- The School of Software Engineering, Beijing University of Technology, Beijing 100024, China; (K.u.R.); (J.L.); (A.Y.); (S.A.); (T.M.)
| | - Jianqiang Li
- The School of Software Engineering, Beijing University of Technology, Beijing 100024, China; (K.u.R.); (J.L.); (A.Y.); (S.A.); (T.M.)
- Beijing Engineering Research Center for IoT Software and Systems, Beijing 100124, China
| | - Yan Pei
- Computer Science Division, University of Aizu, Aizuwakamatsu, Fukushima 965-8580, Japan
- Correspondence:
| | - Anaa Yasin
- The School of Software Engineering, Beijing University of Technology, Beijing 100024, China; (K.u.R.); (J.L.); (A.Y.); (S.A.); (T.M.)
| | - Saqib Ali
- The School of Software Engineering, Beijing University of Technology, Beijing 100024, China; (K.u.R.); (J.L.); (A.Y.); (S.A.); (T.M.)
| | - Tariq Mahmood
- The School of Software Engineering, Beijing University of Technology, Beijing 100024, China; (K.u.R.); (J.L.); (A.Y.); (S.A.); (T.M.)
- Division of Science and Technology, University of Education, Lahore 54000, Pakistan
| |
Collapse
|
5
|
Shaikh TA, Ali R. An intelligent healthcare system for optimized breast cancer diagnosis using harmony search and simulated annealing (HS-SA) algorithm. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100408] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
|
6
|
Basile TMA, Fanizzi A, Losurdo L, Bellotti R, Bottigli U, Dentamaro R, Didonna V, Fausto A, Massafra R, Moschetta M, Tamborra P, Tangaro S, La Forgia D. Microcalcification detection in full-field digital mammograms: A fully automated computer-aided system. Phys Med 2019; 64:1-9. [PMID: 31515007 DOI: 10.1016/j.ejmp.2019.05.022] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 05/08/2019] [Accepted: 05/25/2019] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Microcalcification clusters in mammograms can be considered as early signs of breast cancer. However, their detection is a very challenging task because of different factors: large variety of breast composition, highly textured breast anatomy, impalpable size of microcalcifications in some cases, as well as inherent low contrast of mammograms. Thus, the need to support the clinicians' work with an automatic tool. METHODS In this work a three-phases approach for clustered microcalcification detection is presented. Specifically, it is made up of a pre-processing step, aimed at highlighting potentially interesting breast structures, followed by a single microcalcification detection step, based on Hough transform, that is able to grasp the innate characteristic shape of the structures of interest. Finally, a cluster identification step to group microcalcifications is carried out by means of a clustering algorithm able to codify expert domain rules. RESULTS The detection performance of the proposed method has been evaluated on 364 mammograms of 182 patients obtaining a true positive ratio of 91.78% with 2.87 false positives per image. CONCLUSIONS Experimental results demonstrated that the proposed method is able to detect microcalcification clusters in digital mammograms showing performance comparable to different methodologies exploited in the state-of-art approaches, with the advantage that it does not require any training phase and a large set of data. The performance of the proposed approach remains high even for more difficult clinical cases of mammograms of young women having high-density breast tissue thus resulting in a reduced contrast between microcalcifications and surrounding dense tissues.
Collapse
Affiliation(s)
- T M A Basile
- Department of Physics, University of Bari "Aldo Moro", Bari, Italy; INFN National Institute for Nuclear Physics, Bari Division, Bari, Italy.
| | - A Fanizzi
- I.R.C.C.S. "Giovanni Paolo II" National Cancer Centre, Bari, Italy
| | - L Losurdo
- I.R.C.C.S. "Giovanni Paolo II" National Cancer Centre, Bari, Italy
| | - R Bellotti
- Department of Physics, University of Bari "Aldo Moro", Bari, Italy; INFN National Institute for Nuclear Physics, Bari Division, Bari, Italy
| | - U Bottigli
- Department of Physical Sciences, Earth and Environment, University of Siena, Siena, Italy
| | - R Dentamaro
- I.R.C.C.S. "Giovanni Paolo II" National Cancer Centre, Bari, Italy
| | - V Didonna
- I.R.C.C.S. "Giovanni Paolo II" National Cancer Centre, Bari, Italy
| | - A Fausto
- Department of Diagnostic Imaging, University Hospital of Siena, Siena, Italy
| | - R Massafra
- I.R.C.C.S. "Giovanni Paolo II" National Cancer Centre, Bari, Italy
| | - M Moschetta
- Interdisciplinary Department of Medicine, University of Bari "Aldo Moro", Bari, Italy
| | - P Tamborra
- I.R.C.C.S. "Giovanni Paolo II" National Cancer Centre, Bari, Italy
| | - S Tangaro
- INFN National Institute for Nuclear Physics, Bari Division, Bari, Italy
| | - D La Forgia
- I.R.C.C.S. "Giovanni Paolo II" National Cancer Centre, Bari, Italy
| |
Collapse
|
7
|
|
8
|
An Automatic HEp-2 Specimen Analysis System Based on an Active Contours Model and an SVM Classification. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9020307] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The antinuclear antibody (ANA) test is widely used for screening, diagnosing, and monitoring of autoimmune diseases. The most common methods to determine ANA are indirect immunofluorescence (IIF), performed by human epithelial type 2 (HEp-2) cells, as substrate antigen. The evaluation of ANA consist an analysis of fluorescence intensity and staining patterns. This paper presents a complete and fully automatic system able to characterize IIF images. The fluorescence intensity classification was obtained by performing an image preprocessing phase and implementing a Support Vector Machines (SVM) classifier. The cells identification problem has been addressed by developing a flexible segmentation methods, based on the Hough transform for ellipses, and on an active contours model. In order to classify the HEp-2 cells, six SVM and one k-nearest neighbors (KNN)classifiers were developed. The system was tested on a public database consisting of 2080 IIF images. Unlike almost all work presented on this topic, the proposed system automatically addresses all phases of the HEp-2 image analysis process. All results have been evaluated by comparing them with some of the most representative state-of-the-art work, demonstrating the goodness of the system in the characterization of HEp-2 images.
Collapse
|
9
|
Hernández-Capistrán J, Martínez-Carballido JF, Rosas-Romero R. False Positive Reduction by an Annular Model as a Set of Few Features for Microcalcification Detection to Assist Early Diagnosis of Breast Cancer. J Med Syst 2018; 42:134. [PMID: 29915992 DOI: 10.1007/s10916-018-0989-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2018] [Accepted: 06/07/2018] [Indexed: 10/14/2022]
Abstract
Early automatic breast cancer detection from mammograms is based on the extraction of lesions, known as microcalcifications (MCs). This paper proposes a new and simple system for microcalcification detection to assist in early breast cancer detection. This work uses the two most recognized public mammogram databases, MIAS and DDSM. We are introducing a MC detection method based on (1) Beucher gradient for detection of regions of interest (ROIs), (2) an annulus model for extraction of few and effective features from candidates to MCs, and (3) one classification stage with two different classifiers, k Nearest Neighbor (KNN) and Support Vector Machine (SVM). For dense mammograms in the MIAS database, the performance metrics achieved are sensitivity of 0.9835, false alarm rate of 0.0083, accuracy of 0.9835, and area under the ROC curve of 0.9980 with a KNN classifier. The proposed MC detection method, based on a KNN classifier, achieves, a sensitivity, false positive rate, accuracy and area under the ROC curve of 0.9813, 0.0224, 0.9795 and 0.9974 for the MIAS database; and 0.9035, 0.0439, 0.9298 and 0.9759 for the DDSM database. By slightly reducing the true positive rate the method achieves three instances with false positive rate of 0: 2 on fatty mammograms with KNN and SVM, and one on dense with SVM. The proposed method gives better results than those from state of the art literature, when the mammograms are classified in fatty, fatty-glandular, and dense.
Collapse
Affiliation(s)
- Jonathan Hernández-Capistrán
- Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro # 1, Santa María Tonantzintla, 72840, Puebla, Pue, Mexico.
| | - Jorge F Martínez-Carballido
- Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro # 1, Santa María Tonantzintla, 72840, Puebla, Pue, Mexico
| | | |
Collapse
|
10
|
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.
Collapse
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.
| |
Collapse
|
11
|
Cascio D, Taormina V, Cipolla M, Bruno S, Fauci F, Raso G. A multi-process system for HEp-2 cells classification based on SVM. Pattern Recognit Lett 2016. [DOI: 10.1016/j.patrec.2016.03.024] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
|
12
|
Computer-Assisted Classification Patterns in Autoimmune Diagnostics: The AIDA Project. BIOMED RESEARCH INTERNATIONAL 2016; 2016:2073076. [PMID: 27042658 PMCID: PMC4794569 DOI: 10.1155/2016/2073076] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Revised: 11/24/2015] [Accepted: 01/17/2016] [Indexed: 01/18/2023]
Abstract
Antinuclear antibodies (ANAs) are significant biomarkers in the diagnosis of autoimmune diseases in humans, done by mean of Indirect ImmunoFluorescence (IIF) method, and performed by analyzing patterns and fluorescence intensity. This paper introduces the AIDA Project (autoimmunity: diagnosis assisted by computer) developed in the framework of an Italy-Tunisia cross-border cooperation and its preliminary results. A database of interpreted IIF images is being collected through the exchange of images and double reporting and a Gold Standard database, containing around 1000 double reported images, has been settled. The Gold Standard database is used for optimization of a CAD (Computer Aided Detection) solution and for the assessment of its added value, in order to be applied along with an Immunologist as a second Reader in detection of autoantibodies. This CAD system is able to identify on IIF images the fluorescence intensity and the fluorescence pattern. Preliminary results show that CAD, used as second Reader, appeared to perform better than Junior Immunologists and hence may significantly improve their efficacy; compared with two Junior Immunologists, the CAD system showed higher Intensity Accuracy (85,5% versus 66,0% and 66,0%), higher Patterns Accuracy (79,3% versus 48,0% and 66,2%), and higher Mean Class Accuracy (79,4% versus 56,7% and 64.2%).
Collapse
|
13
|
Tan R, Xiao Y, Tang Q, Zhang Y, Chen H, Fan X. The Diagnostic Value of Micropure Imaging in Breast Suspicious Microcalcificaion. Acad Radiol 2015; 22:1338-43. [PMID: 26329696 DOI: 10.1016/j.acra.2015.07.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2015] [Revised: 07/12/2015] [Accepted: 07/16/2015] [Indexed: 11/29/2022]
Abstract
RATIONALE AND OBJECTIVES The purpose of this study was to evaluate the diagnostic value of Micropure Imaging (MI) in breast lesions differentiation by comparison with B-mode ultrasonography (B-US) and Doppler ultrasonography (DU). MATERIALS AND METHODS A total of 135 consecutive patients (all females) with 135 suspicious lesions were examined and skin marked by MI before mammotome biopsies. All patients (age range, 20-86 years; mean age, 42.5 ± 15.6 years) were the first onset, not in the pregnancy or lactation and had no history of radiation or chemotherapy. The maximum diameter of lesions ranged from 0.2 to 9.6 cm (average 1.98 ± 1.3 cm). Their final diagnoses were obtained by histologic examination. The study protocol was approved by the hospital review board; each patient gave written informed consent. RESULTS One hundred thirty-five breast lesions were classified into 90 nonmalignant and 45 malignant types. To 86 breast lesions with microcalcification, MI showed more microcalcification and coincided better with pathology results than B-US did (P < .05). The specificity of MI was higher than that of B-US and DU; the sensitivity of DU was significantly higher than that of B-US and MI (P < .001). By combining B-US, DU, and MI, the detection accuracy was 86.7%. Receiver-operator characteristic curves showed the area under the curve of B-US, DU, and MI was 0.865, 0.934, and 0.923(P = .000), respectively. Moreover, the interobserver agreements of MI were the highest, 0.922 (observer 1 vs. observer 2), 0.866 (observer 1 vs. observer 3), and 0.916 (observer 2 vs. observer 3). CONCLUSIONS MI as an adjunct ultrasound modality holds some promise in locating and differentiating breast lesions.
Collapse
Affiliation(s)
- Rong Tan
- Department of Ultrasonography, The First Hospital of Changsha, Changsha, Hunan, PR China
| | - Ying Xiao
- Department of Ultrasonography, Xiangya Hospital, Central South University, Xiangya Road 141, Changsha, Hunan, 410008, PR China.
| | - Qi Tang
- Department of Ultrasonography, The First Hospital of Changsha, Changsha, Hunan, PR China
| | - Ying Zhang
- Department of Ultrasonography, The First Hospital of Changsha, Changsha, Hunan, PR China
| | - Hui Chen
- Department of Pathology, The First Hospital of Changsha, Changsha, Hunan, PR China
| | - Xiancheng Fan
- Department of Breast Surgery, the First Hospital of Changsha, Changsha, Hunan, PR China
| |
Collapse
|