1
|
Bilal A, Imran A, Liu X, Liu X, Ahmad Z, Shafiq M, El-Sherbeeny AM, Long H. BC-QNet: A quantum-infused ELM model for breast cancer diagnosis. Comput Biol Med 2024; 175:108483. [PMID: 38704900 DOI: 10.1016/j.compbiomed.2024.108483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 04/11/2024] [Accepted: 04/12/2024] [Indexed: 05/07/2024]
Abstract
The timely and accurate diagnosis of breast cancer is pivotal for effective treatment, but current automated mammography classification methods have their constraints. In this study, we introduce an innovative hybrid model that marries the power of the Extreme Learning Machine (ELM) with FuNet transfer learning, harnessing the potential of the MIAS dataset. This novel approach leverages an Enhanced Quantum-Genetic Binary Grey Wolf Optimizer (Q-GBGWO) within the ELM framework, elevating its performance. Our contributions are twofold: firstly, we employ a feature fusion strategy to optimize feature extraction, significantly enhancing breast cancer classification accuracy. The proposed methodological motivation stems from optimizing feature extraction for improved breast cancer classification accuracy. The Q-GBGWO optimizes ELM parameters, demonstrating its efficacy within the ELM classifier. This innovation marks a considerable advancement beyond traditional methods. Through comparative evaluations against various optimization techniques, the exceptional performance of our Q-GBGWO-ELM model becomes evident. The classification accuracy of the model is exceptionally high, with rates of 96.54 % for Normal, 97.24 % for Benign, and 98.01 % for Malignant classes. Additionally, the model demonstrates a high sensitivity with rates of 96.02 % for Normal, 96.54 % for Benign, and 97.75 % for Malignant classes, and it exhibits impressive specificity with rates of 96.69 % for Normal, 97.38 % for Benign, and 98.16 % for Malignant classes. These metrics are reflected in its ability to classify three different types of breast cancer accurately. Our approach highlights the innovative integration of image data, deep feature extraction, and optimized ELM classification, marking a transformative step in advancing early breast cancer detection and enhancing patient outcomes.
Collapse
Affiliation(s)
- Anas Bilal
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China; Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou, 571158, China
| | - Azhar Imran
- Department of Creative Technologies, Air University, Islamabad, 44000, Pakistan
| | - Xiaowen Liu
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China; Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou, 571158, China
| | - Xiling Liu
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China; Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou, 571158, China
| | - Zohaib Ahmad
- Department of Criminology & Forensic Sciences Technology, Lahore Garrison University, Lahore, Pakistan
| | - Muhammad Shafiq
- School of Information Engineering, Qujing Normal University, Yunnan, China
| | - Ahmed M El-Sherbeeny
- Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh, 11421, Saudi Arabia
| | - Haixia Long
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China; Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou, 571158, China.
| |
Collapse
|
2
|
Rehman SU, Khan MA, Masood A, Almujally NA, Baili J, Alhaisoni M, Tariq U, Zhang YD. BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images. Diagnostics (Basel) 2023; 13:diagnostics13091618. [PMID: 37175009 PMCID: PMC10178634 DOI: 10.3390/diagnostics13091618] [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: 02/27/2023] [Revised: 04/16/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
The early detection of breast cancer using mammogram images is critical for lowering women's mortality rates and allowing for proper treatment. Deep learning techniques are commonly used for feature extraction and have demonstrated significant performance in the literature. However, these features do not perform well in several cases due to redundant and irrelevant information. We created a new framework for diagnosing breast cancer using entropy-controlled deep learning and flower pollination optimization from the mammogram images. In the proposed framework, a filter fusion-based method for contrast enhancement is developed. The pre-trained ResNet-50 model is then improved and trained using transfer learning on both the original and enhanced datasets. Deep features are extracted and combined into a single vector in the following phase using a serial technique known as serial mid-value features. The top features are then classified using neural networks and machine learning classifiers in the following stage. To accomplish this, a technique for flower pollination optimization with entropy control has been developed. The exercise used three publicly available datasets: CBIS-DDSM, INbreast, and MIAS. On these selected datasets, the proposed framework achieved 93.8, 99.5, and 99.8% accuracy, respectively. Compared to the current methods, the increase in accuracy and decrease in computational time are explained.
Collapse
Affiliation(s)
- Shams Ur Rehman
- Department of Computer Science, HITEC University, Taxila 47080, Pakistan
| | | | - Anum Masood
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway
| | - Nouf Abdullah Almujally
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Jamel Baili
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia
| | - Majed Alhaisoni
- College of Computer Science and Engineering, University of Ha'il, Ha'il 81451, Saudi Arabia
| | - Usman Tariq
- Management Information System Department, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia
| | - Yu-Dong Zhang
- Department of Informatics, University of Leicester, Leicester LE1 7RH, UK
| |
Collapse
|
3
|
Jakkaladiki SP, Maly F. An efficient transfer learning based cross model classification (TLBCM) technique for the prediction of breast cancer. PeerJ Comput Sci 2023; 9:e1281. [PMID: 37346575 PMCID: PMC10280457 DOI: 10.7717/peerj-cs.1281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/16/2023] [Indexed: 06/23/2023]
Abstract
Breast cancer has been the most life-threatening disease in women in the last few decades. The high mortality rate among women is due to breast cancer because of less awareness and a minimum number of medical facilities to detect the disease in the early stages. In the recent era, the situation has changed with the help of many technological advancements and medical equipment to observe breast cancer development. The machine learning technique supports vector machines (SVM), logistic regression, and random forests have been used to analyze the images of cancer cells on different data sets. Although the particular technique has performed better on the smaller data set, accuracy still needs to catch up in most of the data, which needs to be fairer to apply in the real-time medical environment. In the proposed research, state-of-the-art deep learning techniques, such as transfer learning, based cross model classification (TLBCM), convolution neural network (CNN) and transfer learning, residual network (ResNet), and Densenet proposed for efficient prediction of breast cancer with the minimized error rating. The convolution neural network and transfer learning are the most prominent techniques for predicting the main features in the data set. The sensitive data is protected using a cyber-physical system (CPS) while using the images virtually over the network. CPS act as a virtual connection between human and networks. While the data is transferred in the network, it must monitor using CPS. The ResNet changes the data on many layers without compromising the minimum error rate. The DenseNet conciliates the problem of vanishing gradient issues. The experiment is carried out on the data sets Breast Cancer Wisconsin (Diagnostic) and Breast Cancer Histopathological Dataset (BreakHis). The convolution neural network and the transfer learning have achieved a validation accuracy of 98.3%. The results of these proposed methods show the highest classification rate between the benign and the malignant data. The proposed method improves the efficiency and speed of classification, which is more convenient for discovering breast cancer in earlier stages than the previously proposed methodologies.
Collapse
|
4
|
Loizidou K, Elia R, Pitris C. Computer-aided breast cancer detection and classification in mammography: A comprehensive review. Comput Biol Med 2023; 153:106554. [PMID: 36646021 DOI: 10.1016/j.compbiomed.2023.106554] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/13/2022] [Accepted: 01/11/2023] [Indexed: 01/15/2023]
Abstract
Cancer is the second cause of mortality worldwide and it has been identified as a perilous disease. Breast cancer accounts for ∼20% of all new cancer cases worldwide, making it a major cause of morbidity and mortality. Mammography is an effective screening tool for the early detection and management of breast cancer. However, the identification and interpretation of breast lesions is challenging even for expert radiologists. For that reason, several Computer-Aided Diagnosis (CAD) systems are being developed to assist radiologists to accurately detect and/or classify breast cancer. This review examines the recent literature on the automatic detection and/or classification of breast cancer in mammograms, using both conventional feature-based machine learning and deep learning algorithms. The review begins with a comparison of algorithms developed specifically for the detection and/or classification of two types of breast abnormalities, micro-calcifications and masses, followed by the use of sequential mammograms for improving the performance of the algorithms. The available Food and Drug Administration (FDA) approved CAD systems related to triage and diagnosis of breast cancer in mammograms are subsequently presented. Finally, a description of the open access mammography datasets is provided and the potential opportunities for future work in this field are highlighted. The comprehensive review provided here can serve both as a thorough introduction to the field but also provide indicative directions to guide future applications.
Collapse
Affiliation(s)
- Kosmia Loizidou
- KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus.
| | - Rafaella Elia
- KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus.
| | - Costas Pitris
- KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus.
| |
Collapse
|
5
|
Loizidou K, Skouroumouni G, Nikolaou C, Pitris C. A Review of Computer-Aided Breast Cancer Diagnosis Using Sequential Mammograms. Tomography 2022; 8:2874-2892. [PMID: 36548533 PMCID: PMC9785714 DOI: 10.3390/tomography8060241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 11/18/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022] Open
Abstract
Radiologists assess the results of mammography, the key screening tool for the detection of breast cancer, to determine the presence of malignancy. They, routinely, compare recent and prior mammographic views to identify changes between the screenings. In case a new lesion appears in a mammogram, or a region is changing rapidly, it is more likely to be suspicious, compared to a lesion that remains unchanged and it is usually benign. However, visual evaluation of mammograms is challenging even for expert radiologists. For this reason, various Computer-Aided Diagnosis (CAD) algorithms are being developed to assist in the diagnosis of abnormal breast findings using mammograms. Most of the current CAD systems do so using only the most recent mammogram. This paper provides a review of the development of methods to emulate the radiological approach and perform automatic segmentation and/or classification of breast abnormalities using sequential mammogram pairs. It begins with demonstrating the importance of utilizing prior views in mammography, through the review of studies where the performance of expert and less-trained radiologists was compared. Following, image registration techniques and their application to mammography are presented. Subsequently, studies that implemented temporal analysis or subtraction of temporally sequential mammograms are summarized. Finally, a description of the open access mammography datasets is provided. This comprehensive review can serve as a thorough introduction to the use of prior information in breast cancer CAD systems but also provides indicative directions to guide future applications.
Collapse
Affiliation(s)
- Kosmia Loizidou
- KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia 2109, Cyprus
| | | | - Christos Nikolaou
- Radiology Department, Limassol General Hospital, Limassol 3304, Cyprus
| | - Costas Pitris
- KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia 2109, Cyprus
| |
Collapse
|
6
|
Loizidou K, Skouroumouni G, Nikolaou C, Pitris C. Automatic Breast Mass Segmentation and Classification Using Subtraction of Temporally Sequential Digital Mammograms. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2022; 10:1801111. [PMID: 36519002 PMCID: PMC9744267 DOI: 10.1109/jtehm.2022.3219891] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 10/10/2022] [Accepted: 10/29/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Cancer remains a major cause of morbidity and mortality globally, with 1 in 5 of all new cancers arising in the breast. The introduction of mammography for the radiological diagnosis of breast abnormalities, significantly decreased their mortality rates. Accurate detection and classification of breast masses in mammograms is especially challenging for various reasons, including low contrast and the normal variations of breast tissue density. Various Computer-Aided Diagnosis (CAD) systems are being developed to assist radiologists with the accurate classification of breast abnormalities. METHODS In this study, subtraction of temporally sequential digital mammograms and machine learning are proposed for the automatic segmentation and classification of masses. The performance of the algorithm was evaluated on a dataset created especially for the purposes of this study, with 320 images from 80 patients (two time points and two views of each breast) with precisely annotated mass locations by two radiologists. RESULTS Ninety-six features were extracted and ten classifiers were tested in a leave-one-patient-out and k-fold cross-validation process. Using Neural Networks, the detection of masses was 99.9% accurate. The classification accuracy of the masses as benign or suspicious increased from 92.6%, using the state-of-the-art temporal analysis, to 98%, using the proposed methodology. The improvement was statistically significant (p-value < 0.05). CONCLUSION These results demonstrate the effectiveness of the subtraction of temporally consecutive mammograms for the diagnosis of breast masses. Clinical and Translational Impact Statement: The proposed algorithm has the potential to substantially contribute to the development of automated breast cancer Computer-Aided Diagnosis systems with significant impact on patient prognosis.
Collapse
Affiliation(s)
- Kosmia Loizidou
- KIOS Research and Innovation Center of ExcellenceDepartment of Electrical and Computer EngineeringUniversity of Cyprus 2109 Nicosia Cyprus
| | | | | | - Costas Pitris
- KIOS Research and Innovation Center of ExcellenceDepartment of Electrical and Computer EngineeringUniversity of Cyprus 2109 Nicosia Cyprus
| |
Collapse
|
7
|
Thawkar S. Feature selection and classification in mammography using hybrid crow search algorithm with Harris hawks optimization. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
|
8
|
CoroNet: Deep Neural Network-Based End-to-End Training for Breast Cancer Diagnosis. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147080] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
In 2020, according to the publications of both the Global Cancer Observatory (GCO) and the World Health Organization (WHO), breast cancer (BC) represents one of the highest prevalent cancers in women worldwide. Almost 47% of the world’s 100,000 people are diagnosed with breast cancer, among females. Moreover, BC prevails among 38.8% of Egyptian women having cancer. Current deep learning developments have shown the common usage of deep convolutional neural networks (CNNs) for analyzing medical images. Unlike the randomly initialized ones, pre-trained natural image database (ImageNet)-based CNN models may become successfully fine-tuned to obtain improved findings. To conduct the automatic detection of BC by the CBIS-DDSM dataset, a CNN model, namely CoroNet, is proposed. It relies on the Xception architecture, which has been pre-trained on the ImageNet dataset and has been fully trained on whole-image BC according to mammograms. The convolutional design method is used in this paper, since it performs better than the other methods. On the prepared dataset, CoroNet was trained and tested. Experiments show that in a four-class classification, it may attain an overall accuracy of 94.92% (benign mass vs. malignant mass) and (benign calcification vs. malignant calcification). CoroNet has a classification accuracy of 88.67% for the two-class cases (calcifications and masses). The paper concluded that there are promising outcomes that could be improved because more training data are available.
Collapse
|
9
|
Breast Cancer Prediction Empowered with Fine-Tuning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5918686. [PMID: 35720929 PMCID: PMC9203172 DOI: 10.1155/2022/5918686] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 05/06/2022] [Indexed: 12/19/2022]
Abstract
In the world, in the past recent five years, breast cancer is diagnosed about 7.8 million women's and making it the most widespread cancer, and it is the second major reason for women's death. So, early prevention and diagnosis systems of breast cancer could be more helpful and significant. Neural networks can extract multiple features automatically and perform predictions on breast cancer. There is a need for several labeled images to train neural networks which is a nonconventional method for some types of data images such as breast magnetic resonance imaging (MRI) images. So, there is only one significant solution for this query is to apply fine-tuning in the neural network. In this paper, we proposed a fine-tuning model using AlexNet in the neural network to extract features from breast cancer images for training purposes. So, in the proposed model, we updated the first and last three layers of AlexNet to detect the normal and abnormal regions of breast cancer. The proposed model is more efficient and significant because, during the training and testing process, the proposed model achieves higher accuracy 98.44% and 98.1% of training and testing, respectively. So, this study shows that the use of fine-tuning in the neural network can detect breast cancer using MRI images and train a neural network classifier by feature extraction using the proposed model is faster and more efficient.
Collapse
|
10
|
Rahimi Rise Z, Mahootchi M, Ahmadi A. Fusing clinical and image data for detecting the severity of breast cancer by a novel hierarchical approach. J EXP THEOR ARTIF IN 2022. [DOI: 10.1080/0952813x.2021.1960629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Zeinab Rahimi Rise
- Department of Industrial Engineering and Management Systems, Amirkabir University of Technology (Tehran Polytechnic), Iran
| | - Masoud Mahootchi
- Department of Industrial Engineering and Management Systems, Amirkabir University of Technology (Tehran Polytechnic), Iran
| | - Abbas Ahmadi
- Department of Industrial Engineering and Management Systems, Amirkabir University of Technology (Tehran Polytechnic), Iran
| |
Collapse
|
11
|
Srinivas A, Prasad VVKDV, Kumari BL. Level set segmentation of mammogram images using adaptive cuckoo K-means clustering. APPLIED NANOSCIENCE 2022. [DOI: 10.1007/s13204-021-02163-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
|
12
|
Mayrovitz HN, Weingrad DN. Tissue Dielectric Constant Differentials between Malignant and Benign Breast Tumors. Clin Breast Cancer 2022; 22:473-477. [DOI: 10.1016/j.clbc.2022.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 01/06/2022] [Accepted: 02/08/2022] [Indexed: 11/03/2022]
|
13
|
Suman AA, Russo C, Carrigan A, Nalepka P, Liquet-Weiland B, Newport RA, Kumari P, Di Ieva A. Spatial and time domain analysis of eye-tracking data during screening of brain magnetic resonance images. PLoS One 2021; 16:e0260717. [PMID: 34855867 PMCID: PMC8639086 DOI: 10.1371/journal.pone.0260717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 11/15/2021] [Indexed: 12/01/2022] Open
Abstract
INTRODUCTION Eye-tracking research has been widely used in radiology applications. Prior studies exclusively analysed either temporal or spatial eye-tracking features, both of which alone do not completely characterise the spatiotemporal dynamics of radiologists' gaze features. PURPOSE Our research aims to quantify human visual search dynamics in both domains during brain stimuli screening to explore the relationship between reader characteristics and stimuli complexity. The methodology can be used to discover strategies to aid trainee radiologists in identifying pathology, and to select regions of interest for machine vision applications. METHOD The study was performed using eye-tracking data 5 seconds in duration from 57 readers (15 Brain-experts, 11 Other-experts, 5 Registrars and 26 Naïves) for 40 neuroradiological images as stimuli (i.e., 20 normal and 20 pathological brain MRIs). The visual scanning patterns were analysed by calculating the fractal dimension (FD) and Hurst exponent (HE) using re-scaled range (R/S) and detrended fluctuation analysis (DFA) methods. The FD was used to measure the spatial geometrical complexity of the gaze patterns, and the HE analysis was used to measure participants' focusing skill. The focusing skill is referred to persistence/anti-persistence of the participants' gaze on the stimulus over time. Pathological and normal stimuli were analysed separately both at the "First Second" and full "Five Seconds" viewing duration. RESULTS All experts were more focused and a had higher visual search complexity compared to Registrars and Naïves. This was seen in both the pathological and normal stimuli in the first and five second analyses. The Brain-experts subgroup was shown to achieve better focusing skill than Other-experts due to their domain specific expertise. Indeed, the FDs found when viewing pathological stimuli were higher than those in normal ones. Viewing normal stimuli resulted in an increase of FD found in five second data, unlike pathological stimuli, which did not change. In contrast to the FDs, the scanpath HEs of pathological and normal stimuli were similar. However, participants' gaze was more focused for "Five Seconds" than "First Second" data. CONCLUSIONS The HE analysis of the scanpaths belonging to all experts showed that they have greater focus than Registrars and Naïves. This may be related to their higher visual search complexity than non-experts due to their training and expertise.
Collapse
Affiliation(s)
- Abdulla Al Suman
- Computational NeuroSurgery (CNS) Lab, Faculty of Medicine, Health, and Human Sciences, Macquarie University, Sydney, Australia
| | - Carlo Russo
- Computational NeuroSurgery (CNS) Lab, Faculty of Medicine, Health, and Human Sciences, Macquarie University, Sydney, Australia
| | - Ann Carrigan
- School of Psychological Sciences, Faculty of Medicine, Health, and Human Sciences, Macquarie University, Sydney, Australia
- Centre for Elite Performance, Expertise and Training, Macquarie University, Sydney, Australia
| | - Patrick Nalepka
- School of Psychological Sciences, Faculty of Medicine, Health, and Human Sciences, Macquarie University, Sydney, Australia
- Centre for Elite Performance, Expertise and Training, Macquarie University, Sydney, Australia
| | - Benoit Liquet-Weiland
- Department of Mathematics and Statistics, Faculty of Science and Engineering, Macquarie University, Sydney, Australia
| | - Robert Ahadizad Newport
- Computational NeuroSurgery (CNS) Lab, Faculty of Medicine, Health, and Human Sciences, Macquarie University, Sydney, Australia
| | - Poonam Kumari
- Computational NeuroSurgery (CNS) Lab, Faculty of Medicine, Health, and Human Sciences, Macquarie University, Sydney, Australia
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab, Faculty of Medicine, Health, and Human Sciences, Macquarie University, Sydney, Australia
- Centre for Elite Performance, Expertise and Training, Macquarie University, Sydney, Australia
| |
Collapse
|
14
|
Aljuaid H, Mahmoud HAH. Methodology for Exploring Patterns of Epigenetic Information in Cancer Cells Using Data Mining Technique. Healthcare (Basel) 2021; 9:1652. [PMID: 34946378 PMCID: PMC8700852 DOI: 10.3390/healthcare9121652] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 11/22/2021] [Accepted: 11/23/2021] [Indexed: 12/12/2022] Open
Abstract
Epigenetic changes are a necessary characteristic of all cancer types. Tumor cells usually target genetic changes and epigenetic alterations as well. It is most beneficial to identify epigenetic similar features among cancer various types to be able to discover the appropriate treatments. The existence of epigenetic alteration profiles can aid in targeting this goal. In this paper, we propose a new technique applying data mining and clustering methodologies for cancer epigenetic changes analysis. The proposed technique aims to detect common patterns of epigenetic changes in various cancer types. We demonstrated the validation of the new technique by detecting epigenetic patterns across seven cancer types and by determining epigenetic similarities among various cancer types. The experimental results demonstrate that common epigenetic patterns do exist across these cancer types. Additionally, epigenetic gene analysis performed on the associated genes found a strong relationship with the development of various types of cancer and proved high risk across the studied cancer types. We utilized the frequent pattern data mining approach to represent cancer types compactly in the promoters for some epigenetic marks. Utilizing the built frequent pattern item set, the most frequent items are identified and yield the group of the bi-clusters of these patterns. Experimental results of the proposed method are shown to have a success rate of 88% in detecting cancer types according to specific epigenetic pattern.
Collapse
Affiliation(s)
- Hanan Aljuaid
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11047, Saudi Arabia;
| | - Hanan A. Hosni Mahmoud
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11047, Saudi Arabia;
- Department of Computer and Systems Engineering, Faculty of Engineering, University of Alexandria, Alexandria 21544, Egypt
| |
Collapse
|
15
|
Thawkar S, Sharma S, Khanna M, Singh LK. Breast cancer prediction using a hybrid method based on Butterfly Optimization Algorithm and Ant Lion Optimizer. Comput Biol Med 2021; 139:104968. [PMID: 34735947 DOI: 10.1016/j.compbiomed.2021.104968] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 10/20/2021] [Accepted: 10/20/2021] [Indexed: 10/20/2022]
Abstract
The design and development of a computer-based system for breast cancer detection are largely reliant on feature selection techniques. These techniques are used to reduce the dimensionality of the feature space by removing irrelevant or redundant features from the original set. This article presents a hybrid feature selection method that is based on the Butterfly optimization algorithm (BOA) and the Ant Lion optimizer (ALO) to form a hybrid BOAALO method. The optimal subset of features chosen by BOAALO is utilized to predict the benign or malignant status of breast tissue using three classifiers: artificial neural network, adaptive neuro-fuzzy inference system, and support vector machine. The goodness of the proposed method is tested using 651 mammogram images. The results show that BOAALO outperforms the original BOA and ALO in terms of accuracy, sensitivity, specificity, kappa value, type-I, and type-II error as well as the receiver operating characteristics curve. Additionally, the suggested method's robustness is assessed and compared to five well-known methods using a benchmark dataset. The experimental findings demonstrate that BOAALO achieves a high degree of accuracy with a minimum number of features. These results support the suggested method's applicability for breast cancer diagnosis.
Collapse
Affiliation(s)
- Shankar Thawkar
- Department of Information Technology, Hindustan College of Science and Technology, Mathura, Uttar Pradesh, India.
| | - Satish Sharma
- Department of Electronics and Computer Science, R. T. M. Nagpur University, Nagpur, Maharashtra, India
| | - Munish Khanna
- Department of Computer Science and Engineering, Hindustan College of Science and Technology, Mathura, Uttar Pradesh, India
| | - Law Kumar Singh
- Department of Computer Science and Engineering, Hindustan College of Science and Technology, Mathura, Uttar Pradesh, India
| |
Collapse
|
16
|
Tardy M, Mateus D. Looking for Abnormalities in Mammograms With Self- and Weakly Supervised Reconstruction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:2711-2722. [PMID: 33417539 DOI: 10.1109/tmi.2021.3050040] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Early breast cancer screening through mammography produces every year millions of images worldwide. Despite the volume of the data generated, these images are not systematically associated with standardized labels. Current protocols encourage giving a malignancy probability to each studied breast but do not require the explicit and burdensome annotation of the affected regions. In this work, we address the problem of abnormality detection in the context of such weakly annotated datasets. We combine domain knowledge about the pathology and clinically available image-wise labels to propose a mixed self- and weakly supervised learning framework for abnormalities reconstruction. We also introduce an auxiliary classification task based on the reconstructed regions to improve explainability. We work with high-resolution imaging that enables our network to capture different findings, including masses, micro-calcifications, distortions, and asymmetries, unlike most state-of-the-art works that mainly focus on masses. We use the popular INBreast dataset as well as our private multi-manufacturer dataset for validation and we challenge our method in segmentation, detection, and classification versus multiple state-of-the-art methods. Our results include image-wise AUC up to 0.86, overall region detection true positives rate of 0.93, and the pixel-wise F1 score of 64% on malignant masses.
Collapse
|
17
|
Loizidou K, Skouroumouni G, Pitris C, Nikolaou C. Digital subtraction of temporally sequential mammograms for improved detection and classification of microcalcifications. Eur Radiol Exp 2021; 5:40. [PMID: 34519867 PMCID: PMC8440760 DOI: 10.1186/s41747-021-00238-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 08/04/2021] [Indexed: 11/24/2022] Open
Abstract
Background Our aim was to demonstrate that automated detection and classification of breast microcalcifications, according to Breast Imaging Reporting and Data System (BI-RADS) categorisation, can be improved with the subtraction of sequential mammograms as opposed to using the most recent image only. Methods One hundred pairs of mammograms were retrospectively collected from two temporally sequential rounds. Fifty percent of the images included no (BI-RADS 1) or benign (BI-RADS 2) microcalcifications. The remaining exhibited suspicious findings (BI-RADS 4-5) in the recent image. Mammograms cannot be directly subtracted, due to tissue changes over time and breast deformation during mammography. To overcome this challenge, optimised preprocessing, image registration, and postprocessing procedures were developed. Machine learning techniques were employed to eliminate false positives (normal tissue misclassified as microcalcifications) and to classify the true microcalcifications as BI-RADS benign or suspicious. Ninety-six features were extracted and nine classifiers were evaluated with and without temporal subtraction. The performance was assessed by measuring sensitivity, specificity, accuracy, and area under the curve (AUC) at receiver operator characteristics analysis. Results Using temporal subtraction, the contrast ratio improved ~ 57 times compared to the most recent mammograms, enhancing the detection of the radiologic changes. Classifying as BI-RADS benign versus suspicious microcalcifications, resulted in 90.3% accuracy and 0.87 AUC, compared to 82.7% and 0.81 using just the most recent mammogram (p = 0.003). Conclusion Compared to using the most recent mammogram alone, temporal subtraction is more effective in the microcalcifications detection and classification and may play a role in automated diagnosis systems. Supplementary Information The online version contains supplementary material available at 10.1186/s41747-021-00238-w.
Collapse
Affiliation(s)
- Kosmia Loizidou
- KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, 1 Panepistimiou Avenue, Aglantzia, 2109, Nicosia, Cyprus.
| | - Galateia Skouroumouni
- Nicosia General Hospital, 215 Nicosia-Limassol Old Road, Strovolos, 2029, Nicosia, Cyprus
| | - Costas Pitris
- KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, 1 Panepistimiou Avenue, Aglantzia, 2109, Nicosia, Cyprus
| | | |
Collapse
|
18
|
Janghel R, Rathore Y. Deep Convolution Neural Network Based System for Early Diagnosis of Alzheimer's Disease. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2020.06.006] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
19
|
Akram T, Attique M, Gul S, Shahzad A, Altaf M, Naqvi SSR, Damaševičius R, Maskeliūnas R. A novel framework for rapid diagnosis of COVID-19 on computed tomography scans. Pattern Anal Appl 2021; 24:951-964. [PMID: 33500681 PMCID: PMC7819829 DOI: 10.1007/s10044-020-00950-0] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 12/09/2020] [Indexed: 12/17/2022]
Abstract
Since the emergence of COVID-19, thousands of people undergo chest X-ray and computed tomography scan for its screening on everyday basis. This has increased the workload on radiologists, and a number of cases are in backlog. This is not only the case for COVID-19, but for the other abnormalities needing radiological diagnosis as well. In this work, we present an automated technique for rapid diagnosis of COVID-19 on computed tomography images. The proposed technique consists of four primary steps: (1) data collection and normalization, (2) extraction of the relevant features, (3) selection of the most optimal features and (4) feature classification. In the data collection step, we collect data for several patients from a public domain website, and perform preprocessing, which includes image resizing. In the successive step, we apply discrete wavelet transform and extended segmentation-based fractal texture analysis methods for extracting the relevant features. This is followed by application of an entropy controlled genetic algorithm for selection of the best features from each feature type, which are combined using a serial approach. In the final phase, the best features are subjected to various classifiers for the diagnosis. The proposed framework, when augmented with the Naive Bayes classifier, yields the best accuracy of 92.6%. The simulation results are supported by a detailed statistical analysis as a proof of concept.
Collapse
Affiliation(s)
- Tallha Akram
- Department of EE, COMSATS University Islamabad, Wah Campus, Pakistan
| | - Muhammad Attique
- Department of Computer Science, HITEC University Taxila, Rawalpindi, Pakistan
| | - Salma Gul
- Department of Radiology, Wah Medical College, POF Hospital, Wah Cantt, Rawalpindi, Punjab Pakistan
| | - Aamir Shahzad
- Department of EE, COMSATS University Islamabad, Abbottabad Campus, Pakistan
| | - Muhammad Altaf
- Department of EE, COMSATS University Islamabad, Wah Campus, Pakistan
| | | | | | - Rytis Maskeliūnas
- Department of Applied Informatics, Vytautas Magnus University, Kaunas, Lithuania
| |
Collapse
|
20
|
BENHASSINE NASSEREDINNE, BOUKAACHE ABDELNOUR, BOUDJEHEM DJALIL. A NEW CAD SYSTEM FOR BREAST CANCER CLASSIFICATION USING DISCRIMINATION POWER ANALYSIS OF WAVELET’S COEFFICIENTS AND SUPPORT VECTOR MACHINE. J MECH MED BIOL 2020. [DOI: 10.1142/s0219519420500360] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The Computer-Aided Diagnostic (CAD) system is an important tool that helps radiologists to provide a second opinion for the early detection of breast cancer and therefore, aids to reduce the mortality rates. In this work, we try to develop a new (CAD) system to classify mammograms into benign or malignant. The proposed system consists of three main steps. The preprocessing stage consists of noise filtering, elimination of unwanted objects and suppressing the pectoral muscle. The Seeded Region Growing (SRG) segmentation technique is applied in a triangular region that contains the pectoral muscle to localize it and extract the region of interest (ROI). The features extraction step is performed by applying the discrete wavelet transform (DWT) to each obtained ROI, and the most discriminating coefficients are selected using the discrimination power analysis (DPA) method. Finally, the classification is carried out by the support vector machine (SVM), artificial neural networks (ANN), random forest (RF) and Naive Bayes (NB) classifiers. The evaluation of the proposed system on the mini-MIAS database shows its effectiveness compared to other recently published CAD systems, and a classification accuracy of about 99.41% with the SVM classifier was obtained.
Collapse
Affiliation(s)
- NASSER EDINNE BENHASSINE
- Advanced Control Laboratory (LABCAV), Université 8 Mai 1945 Guelma, B.P. 401 Guelma 24000, Algeria
| | - ABDELNOUR BOUKAACHE
- Advanced Control Laboratory (LABCAV), Université 8 Mai 1945 Guelma, B.P. 401 Guelma 24000, Algeria
| | - DJALIL BOUDJEHEM
- Advanced Control Laboratory (LABCAV), Université 8 Mai 1945 Guelma, B.P. 401 Guelma 24000, Algeria
| |
Collapse
|
21
|
Das P, Das A. Shift invariant extrema based feature analysis scheme to discriminate the spiculation nature of mammograms. ISA TRANSACTIONS 2020; 103:156-165. [PMID: 32216985 DOI: 10.1016/j.isatra.2020.03.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 03/11/2020] [Accepted: 03/12/2020] [Indexed: 06/10/2023]
Abstract
Since uncontrolled growth of malignant masses introduces uneven shape irregularities and spiculations in the boundary, shape representing shift invariant features are essential to resolve the problem of discrimination. However, ambiguous nature of shape, size, margin, orientation of masses produces imprecise feature values. In this view, a new concept of extrema based feature characterization scheme is proposed for capturing radiating nature of mass morphology. Computation of extrema patterns needs only few algorithmic steps. Beside this, present study employs an automated enhancement procedure to improve the classification accuracy. Experimental results show that extrema characterization reduces the feature redundancy to produce high efficiency in reasonably low time.
Collapse
Affiliation(s)
- Poulomi Das
- OmDayal Group of Institutions, Maulana Abul Kalam Azad University of Technology, India.
| | - Arpita Das
- Department of Radio Physics and Electronics, University of Calcutta, Rajabazar Science College Campus, India.
| |
Collapse
|
22
|
Gnanasekaran VS, Joypaul S, Sundaram PM. A Survey on Machine Learning Algorithms for the Diagnosis of Breast Masses with Mammograms. Curr Med Imaging 2020; 16:639-652. [DOI: 10.2174/1573405615666190903141554] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Revised: 07/08/2019] [Accepted: 07/17/2019] [Indexed: 01/22/2023]
Abstract
Breast cancer is leading cancer among women for the past 60 years. There are no effective
mechanisms for completely preventing breast cancer. Rather it can be detected at its earlier
stages so that unnecessary biopsy can be reduced. Although there are several imaging modalities
available for capturing the abnormalities in breasts, mammography is the most commonly used
technique, because of its low cost. Computer-Aided Detection (CAD) system plays a key role in
analyzing the mammogram images to diagnose the abnormalities. CAD assists the radiologists for
diagnosis. This paper intends to provide an outline of the state-of-the-art machine learning algorithms
used in the detection of breast cancer developed in recent years. We begin the review with
a concise introduction about the fundamental concepts related to mammograms and CAD systems.
We then focus on the techniques used in the diagnosis of breast cancer with mammograms.
Collapse
Affiliation(s)
| | - Sutha Joypaul
- AAA College of Engineering and Technology, Sivakasi 626123, Virudhunagar District, Tamil Nadu, India
| | | |
Collapse
|
23
|
Bruno A, Ardizzone E, Vitabile S, Midiri M. A Novel Solution Based on Scale Invariant Feature Transform Descriptors and Deep Learning for the Detection of Suspicious Regions in Mammogram Images. JOURNAL OF MEDICAL SIGNALS & SENSORS 2020; 10:158-173. [PMID: 33062608 PMCID: PMC7528986 DOI: 10.4103/jmss.jmss_31_19] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Revised: 10/01/2019] [Accepted: 05/06/2020] [Indexed: 12/24/2022]
Abstract
BACKGROUND Deep learning methods have become popular for their high-performance rate in the classification and detection of events in computer vision tasks. Transfer learning paradigm is widely adopted to apply pretrained convolutional neural network (CNN) on medical domains overcoming the problem of the scarcity of public datasets. Some investigations to assess transfer learning knowledge inference abilities in the context of mammogram screening and possible combinations with unsupervised techniques are in progress. METHODS We propose a novel technique for the detection of suspicious regions in mammograms that consist of the combination of two approaches based on scale invariant feature transform (SIFT) keypoints and transfer learning with pretrained CNNs such as PyramidNet and AlexNet fine-tuned on digital mammograms generated by different mammography devices. Preprocessing, feature extraction, and selection steps characterize the SIFT-based method, while the deep learning network validates the candidate suspicious regions detected by the SIFT method. RESULTS The experiments conducted on both mini-MIAS dataset and our new public dataset Suspicious Region Detection on Mammogram from PP (SuReMaPP) of 384 digital mammograms exhibit high performances compared to several state-of-the-art methods. Our solution reaches 98% of sensitivity and 90% of specificity on SuReMaPP and 94% of sensitivity and 91% of specificity on mini-MIAS. CONCLUSIONS The experimental sessions conducted so far prompt us to further investigate the powerfulness of transfer learning over different CNNs and possible combinations with unsupervised techniques. Transfer learning performances' accuracy may decrease when the training and testing images come out from mammography devices with different properties.
Collapse
Affiliation(s)
- Alessandro Bruno
- Faculty of Media and Communication, Department - NCCA (National Centre for Computer Animation) at Bournemouth University, Poole, Dorset, United Kingdom
| | | | - Salvatore Vitabile
- Department of Biomedicine, Neuroscience and Advanced Diagnostic at Palermo University, Palermo, Italy
| | - Massimo Midiri
- Department of Biomedicine, Neuroscience and Advanced Diagnostic at Palermo University, Palermo, Italy
| |
Collapse
|
24
|
Heutink F, Koch V, Verbist B, van der Woude WJ, Mylanus E, Huinck W, Sechopoulos I, Caballo M. Multi-Scale deep learning framework for cochlea localization, segmentation and analysis on clinical ultra-high-resolution CT images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 191:105387. [PMID: 32109685 DOI: 10.1016/j.cmpb.2020.105387] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 02/07/2020] [Accepted: 02/11/2020] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Performing patient-specific, pre-operative cochlea CT-based measurements could be helpful to positively affect the outcome of cochlear surgery in terms of intracochlear trauma and loss of residual hearing. Therefore, we propose a method to automatically segment and measure the human cochlea in clinical ultra-high-resolution (UHR) CT images, and investigate differences in cochlea size for personalized implant planning. METHODS 123 temporal bone CT scans were acquired with two UHR-CT scanners, and used to develop and validate a deep learning-based system for automated cochlea segmentation and measurement. The segmentation algorithm is composed of two major steps (detection and pixel-wise classification) in cascade, and aims at combining the results of a multi-scale computer-aided detection scheme with a U-Net-like architecture for pixelwise classification. The segmentation results were used as an input to the measurement algorithm, which provides automatic cochlear measurements (volume, basal diameter, and cochlear duct length (CDL)) through the combined use of convolutional neural networks and thinning algorithms. Automatic segmentation was validated against manual annotation, by the means of Dice similarity, Boundary-F1 (BF) score, and maximum and average Hausdorff distances, while measurement errors were calculated between the automatic results and the corresponding manually obtained ground truth on a per-patient basis. Finally, the developed system was used to investigate the differences in cochlea size within our patient cohort, to relate the measurement errors to the actual variation in cochlear size across different patients. RESULTS Automatic segmentation resulted in a Dice of 0.90 ± 0.03, BF score of 0.95 ± 0.03, and maximum and average Hausdorff distance of 3.05 ± 0.39 and 0.32 ± 0.07 against manual annotation. Automatic cochlear measurements resulted in errors of 8.4% (volume), 5.5% (CDL), 7.8% (basal diameter). The cochlea size varied broadly, ranging between 0.10 and 0.28 ml (volume), 1.3 and 2.5 mm (basal diameter), and 27.7 and 40.1 mm (CDL). CONCLUSIONS The proposed algorithm could successfully segment and analyze the cochlea on UHR-CT images, resulting in accurate measurements of cochlear anatomy. Given the wide variation in cochlear size found in our patient cohort, it may find application as a pre-operative tool in cochlear implant surgery, potentially helping elaborate personalized treatment strategies based on patient-specific, image-based anatomical measurements.
Collapse
Affiliation(s)
- Floris Heutink
- Department of Otorhinolaryngology and Donders Institute for Brain, Cognition and Behavior, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands
| | - Valentin Koch
- Department of Radiology and Nuclear Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands
| | - Berit Verbist
- Department of Radiology, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA, Leiden, the Netherlands
| | - Willem Jan van der Woude
- Department of Radiology and Nuclear Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands
| | - Emmanuel Mylanus
- Department of Otorhinolaryngology and Donders Institute for Brain, Cognition and Behavior, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands
| | - Wendy Huinck
- Department of Otorhinolaryngology and Donders Institute for Brain, Cognition and Behavior, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands
| | - Ioannis Sechopoulos
- Department of Radiology and Nuclear Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands; Dutch Expert Center for Screening (LRCB), Wijchenseweg 101, 6538 SW, Nijmegen, the Netherlands
| | - Marco Caballo
- Department of Radiology and Nuclear Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands.
| |
Collapse
|
25
|
Sechopoulos I, Teuwen J, Mann R. Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: State of the art. Semin Cancer Biol 2020; 72:214-225. [PMID: 32531273 DOI: 10.1016/j.semcancer.2020.06.002] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 05/19/2020] [Accepted: 06/01/2020] [Indexed: 02/07/2023]
Abstract
Screening for breast cancer with mammography has been introduced in various countries over the last 30 years, initially using analog screen-film-based systems and, over the last 20 years, transitioning to the use of fully digital systems. With the introduction of digitization, the computer interpretation of images has been a subject of intense interest, resulting in the introduction of computer-aided detection (CADe) and diagnosis (CADx) algorithms in the early 2000's. Although they were introduced with high expectations, the potential improvement in the clinical realm failed to materialize, mostly due to the high number of false positive marks per analyzed image. In the last five years, the artificial intelligence (AI) revolution in computing, driven mostly by deep learning and convolutional neural networks, has also pervaded the field of automated breast cancer detection in digital mammography and digital breast tomosynthesis. Research in this area first involved comparison of its capabilities to that of conventional CADe/CADx methods, which quickly demonstrated the potential of this new technology. In the last couple of years, more mature and some commercial products have been developed, and studies of their performance compared to that of experienced breast radiologists are showing that these algorithms are on par with human-performance levels in retrospective data sets. Although additional studies, especially prospective evaluations performed in the real screening environment, are needed, it is becoming clear that AI will have an important role in the future breast cancer screening realm. Exactly how this new player will shape this field remains to be determined, but recent studies are already evaluating different options for implementation of this technology. The aim of this review is to provide an overview of the basic concepts and developments in the field AI for breast cancer detection in digital mammography and digital breast tomosynthesis. The pitfalls of conventional methods, and how these are, for the most part, avoided by this new technology, will be discussed. Importantly, studies that have evaluated the current capabilities of AI and proposals for how these capabilities should be leveraged in the clinical realm will be reviewed, while the questions that need to be answered before this vision becomes a reality are posed.
Collapse
Affiliation(s)
- Ioannis Sechopoulos
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, the Netherlands; Dutch Expert Centre for Screening (LRCB), Wijchenseweg 101, 6538 SW, Nijmegen, the Netherlands.
| | - Jonas Teuwen
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, the Netherlands; Department of Radiation Oncology, Netherlands Cancer Institute (NKI), Plesmanlaan 121, 1066 CX, Amsterdam, the Netherlands.
| | - Ritse Mann
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, the Netherlands; Department of Radiology, Netherlands Cancer Institute (NKI), Plesmanlaan 121, 1066 CX, Amsterdam, the Netherlands.
| |
Collapse
|
26
|
Celik Y, Talo M, Yildirim O, Karabatak M, Acharya UR. Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2020.03.011] [Citation(s) in RCA: 114] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
|
27
|
Ramadan SZ. Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review. JOURNAL OF HEALTHCARE ENGINEERING 2020; 2020:9162464. [PMID: 32300474 PMCID: PMC7091549 DOI: 10.1155/2020/9162464] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 12/25/2019] [Accepted: 02/13/2020] [Indexed: 12/28/2022]
Abstract
According to the American Cancer Society's forecasts for 2019, there will be about 268,600 new cases in the United States with invasive breast cancer in women, about 62,930 new noninvasive cases, and about 41,760 death cases from breast cancer. As a result, there is a high demand for breast imaging specialists as indicated in a recent report for the Institute of Medicine and National Research Council. One way to meet this demand is through developing Computer-Aided Diagnosis (CAD) systems for breast cancer detection and diagnosis using mammograms. This study aims to review recent advancements and developments in CAD systems for breast cancer detection and diagnosis using mammograms and to give an overview of the methods used in its steps starting from preprocessing and enhancement step and ending in classification step. The current level of performance for the CAD systems is encouraging but not enough to make CAD systems standalone detection and diagnose clinical systems. Unless the performance of CAD systems enhanced dramatically from its current level by enhancing the existing methods, exploiting new promising methods in pattern recognition like data augmentation in deep learning and exploiting the advances in computational power of computers, CAD systems will continue to be a second opinion clinical procedure.
Collapse
Affiliation(s)
- Saleem Z. Ramadan
- Department of Industrial Engineering, German Jordanian University, Mushaqar 11180, Amman, Jordan
| |
Collapse
|
28
|
Gómez-Flores W, Hernández-López J. Assessment of the invariance and discriminant power of morphological features under geometric transformations for breast tumor classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 185:105173. [PMID: 31710986 DOI: 10.1016/j.cmpb.2019.105173] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 10/28/2019] [Accepted: 10/31/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVES Computer-aided diagnosis (CAD) systems are intended to assist specialists in the interpretation of images aiming to support clinical conduct. In breast tumor classification, CAD systems involve a feature extraction stage, in which morphological features are used to describe the tumor shape. Such features are expected to satisfy at least two conditions: (1) discriminant to distinguish between benign and malignant tumors, and (2) invariant to geometric transformations. Herein, 39 morphological features were evaluated in terms of invariance and discriminant power for breast tumor classification. METHODS Morphological features were divided into region-based features, for describing the irregularity of the tumor shape, and boundary-based features, for measuring the anfractuosity of the tumor margin. Also, two datasets were considered in the experiments: 2054 breast ultrasound images and 892 mammographies. From both datasets, synthetic data augmentation was performed to obtain distinct combinations of rotation and scaling of breast tumors, from which morphological features were calculated. The linear discriminant analysis was used to classify breast tumors in benign and malignant classes. The area under the ROC curve (AUC) quantified the discriminant power of every morphological feature, whereas the relative difference (RD) between AUC values measured the invariance to geometric transformations. For indicating adequate performance, AUC and RD should tend toward unity and zero, respectively. RESULTS For both datasets, the convexity was the most discriminant feature that reached AUC > 0.81 with RD<1×10-2, while the most invariant feature was the roundness that attained RD<1×10-3 with AUC < 0.72. Additionally, for each dataset, the most discriminant and invariant features were combined for performing tumor classification. For mammography, it was achieved accuracy (ACC) of 0.76, sensitivity (SEN) of 0.76, and specificity (SPE) of 0.84, whereas for breast ultrasound the results were ACC=0.88,SEN=0.81, and SPE=0.91. CONCLUSIONS In general, region-based features are more discriminant and invariant than boundary-based features. Moreover, it was observed that an invariant feature is not necessarily a discriminant feature; hence, a balance between invariance and discriminant power should be attained for breast tumor classification.
Collapse
Affiliation(s)
- Wilfrido Gómez-Flores
- Center for Research and Advanced Studies of the National Polytechnic Institute, Ciudad Victoria, Tamaulipas ZIP 87138, Mexico.
| | - Juanita Hernández-López
- Center for Research and Advanced Studies of the National Polytechnic Institute, Ciudad Victoria, Tamaulipas ZIP 87138, Mexico
| |
Collapse
|
29
|
Perre AC, Alexandre LA, Freire LC. Lesion classification in mammograms using convolutional neural networks and transfer learning. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2019. [DOI: 10.1080/21681163.2018.1498392] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Ana C. Perre
- Instituto de Telecomunicações and Faculdade Ciências da Saúde, Universidade da Beira Interior, Covilhã, Portugal
| | - Luís A. Alexandre
- Instituto de Telecomunicações and Departamento de Informática, Universidade da Beira Interior, Covilhã, Portugal
| | - Luís C. Freire
- Escola Superior de Tecnologia da Saúde de Lisboa, Instituto Politécnico de Lisboa, Lisboa, Portugal
| |
Collapse
|
30
|
Bliznakova K, Dukov N, Feradov F, Gospodinova G, Bliznakov Z, Russo P, Mettivier G, Bosmans H, Cockmartin L, Sarno A, Kostova-Lefterova D, Encheva E, Tsapaki V, Bulyashki D, Buliev I. Development of breast lesions models database. Phys Med 2019; 64:293-303. [PMID: 31387779 DOI: 10.1016/j.ejmp.2019.07.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 07/01/2019] [Accepted: 07/22/2019] [Indexed: 12/11/2022] Open
Abstract
PURPOSE We present the development and the current state of the MaXIMA Breast Lesions Models Database, which is intended to provide researchers with both segmented and mathematical computer-based breast lesion models with realistic shape. METHODS The database contains various 3D images of breast lesions of irregular shapes, collected from routine patient examinations or dedicated scientific experiments. It also contains images of simulated tumour models. In order to extract the 3D shapes of the breast cancers from patient images, an in-house segmentation algorithm was developed for the analysis of 50 tomosynthesis sets from patients diagnosed with malignant and benign lesions. In addition, computed tomography (CT) scans of three breast mastectomy cases were added, as well as five whole-body CT scans. The segmentation algorithm includes a series of image processing operations and region-growing techniques with minimal interaction from the user, with the purpose of finding and segmenting the areas of the lesion. Mathematically modelled computational breast lesions, also stored in the database, are based on the 3D random walk approach. RESULTS The MaXIMA Imaging Database currently contains 50 breast cancer models obtained by segmentation of 3D patient breast tomosynthesis images; 8 models obtained by segmentation of whole body and breast cadavers CT images; and 80 models based on a mathematical algorithm. Each record in the database is supported with relevant information. Two applications of the database are highlighted: inserting the lesions into computationally generated breast phantoms and an approach in generating mammography images with variously shaped breast lesion models from the database for evaluation purposes. Both cases demonstrate the implementation of multiple scenarios and of an unlimited number of cases, which can be used for further software modelling and investigation of breast imaging techniques. The created database interface is web-based, user friendly and is intended to be made freely accessible through internet after the completion of the MaXIMA project. CONCLUSIONS The developed database will serve as an imaging data source for researchers, working on breast diagnostic imaging and on improving early breast cancer detection techniques, using existing or newly developed imaging modalities.
Collapse
Affiliation(s)
- Kristina Bliznakova
- Laboratory of Computer Simulations in Medicine, Technical University of Varna, Varna, Bulgaria.
| | - Nikolay Dukov
- Laboratory of Computer Simulations in Medicine, Technical University of Varna, Varna, Bulgaria
| | - Firgan Feradov
- Laboratory of Computer Simulations in Medicine, Technical University of Varna, Varna, Bulgaria
| | - Galja Gospodinova
- Laboratory of Computer Simulations in Medicine, Technical University of Varna, Varna, Bulgaria
| | - Zhivko Bliznakov
- Laboratory of Computer Simulations in Medicine, Technical University of Varna, Varna, Bulgaria
| | - Paolo Russo
- Dipartimento di Fisica "Ettore Pancini", Universita' di Napoli Federico II and INFN Sezione di Napoli, Napoli, Italy
| | - Giovanni Mettivier
- Dipartimento di Fisica "Ettore Pancini", Universita' di Napoli Federico II and INFN Sezione di Napoli, Napoli, Italy
| | - Hilde Bosmans
- Department of Radiology, Katholieke University of Leuven, Leuven, Belgium
| | - Lesley Cockmartin
- Department of Radiology, Katholieke University of Leuven, Leuven, Belgium
| | - Antonio Sarno
- Dipartimento di Fisica "Ettore Pancini", Universita' di Napoli Federico II and INFN Sezione di Napoli, Napoli, Italy
| | | | - Elitsa Encheva
- Radiotherapy Department, University Hospital "St. Marina", Medical University of Varna, Varna, Bulgaria
| | - Virginia Tsapaki
- Medical Physics Department, Konstantopoulio General Hospital, Nea Ionia, Attiki, Greece
| | - Daniel Bulyashki
- Surgery Department, University Hospital "St. Marina", Medical University of Varna, Varna, Bulgaria
| | - Ivan Buliev
- Laboratory of Computer Simulations in Medicine, Technical University of Varna, Varna, Bulgaria
| |
Collapse
|
31
|
Mohanty F, Rup S, Dash B, Majhi B, Swamy MNS. Digital mammogram classification using 2D-BDWT and GLCM features with FOA-based feature selection approach. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04186-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|
32
|
Min H, Chandra SS, Crozier S, Bradley AP. Multi-scale sifting for mammographic mass detection and segmentation. Biomed Phys Eng Express 2019. [DOI: 10.1088/2057-1976/aafc07] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
33
|
Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2018:5940436. [PMID: 30356422 PMCID: PMC6178513 DOI: 10.1155/2018/5940436] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Revised: 06/18/2018] [Accepted: 08/08/2018] [Indexed: 11/18/2022]
Abstract
Breast Cancer is the most prevalent cancer among women across the globe. Automatic detection of breast cancer using Computer Aided Diagnosis (CAD) system suffers from false positives (FPs). Thus, reduction of FP is one of the challenging tasks to improve the performance of the diagnosis systems. In the present work, new FP reduction technique has been proposed for breast cancer diagnosis. It is based on appropriate integration of preprocessing, Self-organizing map (SOM) clustering, region of interest (ROI) extraction, and FP reduction. In preprocessing, contrast enhancement of mammograms has been achieved using Local Entropy Maximization algorithm. The unsupervised SOM clusters an image into number of segments to identify the cancerous region and extracts tumor regions (i.e., ROIs). However, it also detects some FPs which affects the efficiency of the algorithm. Therefore, to reduce the FPs, the output of the SOM is given to the FP reduction step which is aimed to classify the extracted ROIs into normal and abnormal class. FP reduction consists of feature mining from the ROIs using proposed local sparse curvelet coefficients followed by classification using artificial neural network (ANN). The performance of proposed algorithm has been validated using the local datasets as TMCH (Tata Memorial Cancer Hospital) and publicly available MIAS (Suckling et al., 1994) and DDSM (Heath et al., 2000) database. The proposed technique results in reduction of FPs from 0.85 to 0.02 FP/image for MIAS, 4.81 to 0.16 FP/image for DDSM, and 2.32 to 0.05 FP/image for TMCH reflecting huge improvement in classification of mammograms.
Collapse
|
34
|
Sapate SG, Mahajan A, Talbar SN, Sable N, Desai S, Thakur M. Radiomics based detection and characterization of suspicious lesions on full field digital mammograms. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 163:1-20. [PMID: 30119844 DOI: 10.1016/j.cmpb.2018.05.017] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Revised: 05/11/2018] [Accepted: 05/15/2018] [Indexed: 02/05/2023]
Abstract
BACKGROUND AND OBJECTIVE Early detection is the important key to reduce breast cancer mortality rate. Detecting the mammographic abnormality as a subtle sign of breast cancer is essential for the proper diagnosis and treatment. The aim of this preliminary study is to develop algorithms which detect suspicious lesions and characterize them to reduce the diagnostic errors regarding false positives and false negatives. METHODS The proposed hybrid mechanism detects suspicious lesions automatically using connected component labeling and adaptive fuzzy region growing algorithm. A novel neighboring pixel selection algorithm reduces the computational complexity of the seeded region growing algorithm used to finalize lesion contours. These lesions are characterized using radiomic features and then classified as benign mass or malignant tumor using k-NN and SVM classifiers. Two datasets of 460 full field digital mammograms (FFDM) utilized in this clinical study consists of 210 images with malignant tumors, 30 with benign masses and 220 normal breast images that are validated by radiologists expert in mammography. RESULTS The qualitative assessment of segmentation results by the expert radiologists shows 91.67% sensitivity and 58.33% specificity. The effects of seven geometric and 48 textural features on classification accuracy, false positives per image (FPsI), sensitivity and specificity are studied separately and together. The features together achieved the sensitivity of 84.44% and 85.56%, specificity of 91.11% and 91.67% with FPsI of 0.54 and 0.55 using k-NN and SVM classifiers respectively on local dataset. CONCLUSIONS The overall breast cancer detection performance of proposed scheme after combining geometric and textural features with both classifiers is improved in terms of sensitivity, specificity, and FPsI.
Collapse
Affiliation(s)
- Suhas G Sapate
- Centre of Excellence in Signal & Image Processing, SGGS Institute of Engineering & Technology, Nanded, Maharashtra, India; Department of CSE, Ashokrao Mane Group of Institutions, Vathar, Kolhapur, Maharashtra, India.
| | - Abhishek Mahajan
- Department of Radiodiagnosis, Tata Memorial Centre, Parel, Mumbai, Maharashtra, India
| | - Sanjay N Talbar
- Centre of Excellence in Signal & Image Processing, SGGS Institute of Engineering & Technology, Nanded, Maharashtra, India; Department of E&TC, SGGS Institute of Engineering & Technology, Nanded, Maharashtra, India
| | - Nilesh Sable
- Department of Radiodiagnosis, Tata Memorial Centre, Parel, Mumbai, Maharashtra, India
| | - Subhash Desai
- Department of Radiodiagnosis, Tata Memorial Centre, Parel, Mumbai, Maharashtra, India
| | - Meenakshi Thakur
- Department of Radiodiagnosis, Tata Memorial Centre, Parel, Mumbai, Maharashtra, India
| |
Collapse
|
35
|
Dinapoli N, Alitto AR, Vallati M, Gatta R, Autorino R, Boldrini L, Damiani A, Valentini V. Moddicom: a complete and easily accessible library for prognostic evaluations relying on image features. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2015:771-4. [PMID: 26736376 DOI: 10.1109/embc.2015.7318476] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Decision Support Systems (DSSs) are increasingly exploited in the area of prognostic evaluations. For predicting the effect of therapies on patients, the trend is now to use image features, i.e. information that can be automatically computed by considering images resulting by analysis. The DSSs application as predictive tools is particularly suitable for cancer treatment, given the peculiarities of the disease -which is highly localised and lead to significant social costs- and the large number of images that are available for each patient. At the state of the art, there exists tools that allow to handle image features for prognostic evaluations, but they are not designed for medical experts. They require either a strong engineering or computer science background since they do not integrate all the required functions, such as image retrieval and storage. In this paper we fill this gap by proposing Moddicom, a user-friendly complete library specifically designed to be exploited by physicians. A preliminary experimental analysis, performed by a medical expert that used the tool, demonstrates the efficiency and the effectiveness of Moddicom.
Collapse
|
36
|
Berbar MA. Hybrid methods for feature extraction for breast masses classification. EGYPTIAN INFORMATICS JOURNAL 2018. [DOI: 10.1016/j.eij.2017.08.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
37
|
Verma G, Luciani ML, Palombo A, Metaxa L, Panzironi G, Pediconi F, Giuliani A, Bizzarri M, Todde V. Microcalcification morphological descriptors and parenchyma fractal dimension hierarchically interact in breast cancer: A diagnostic perspective. Comput Biol Med 2018; 93:1-6. [DOI: 10.1016/j.compbiomed.2017.12.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 12/09/2017] [Accepted: 12/09/2017] [Indexed: 12/26/2022]
|
38
|
|
39
|
Vikhe PS, Thool VR. Detection and Segmentation of Pectoral Muscle on MLO-View Mammogram Using Enhancement Filter. J Med Syst 2017; 41:190. [DOI: 10.1007/s10916-017-0839-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Accepted: 10/11/2017] [Indexed: 10/18/2022]
|
40
|
Mendel KR, Li H, Lan L, Cahill CM, Rael V, Abe H, Giger ML. Quantitative texture analysis: robustness of radiomics across two digital mammography manufacturers' systems. J Med Imaging (Bellingham) 2017; 5:011002. [PMID: 28948196 DOI: 10.1117/1.jmi.5.1.011002] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2017] [Accepted: 07/10/2017] [Indexed: 11/14/2022] Open
Abstract
The robustness of radiomic texture analysis across different manufacturers of mammography imaging systems is investigated. We quantified feature robustness across mammography manufacturers using a dataset of 111 women who underwent consecutive screening mammography on both general electric and Hologic systems. In each mammogram, a square region of interest (ROI) directly behind the nipple was manually selected. Radiomic features describing parenchymal patterns were automatically extracted on each ROI. Feature comparisons were conducted between manufacturers (and breast densities) using newly developed robustness metrics descriptive of correlation, equivalence, and variability. By examining the distribution of these metric values, we propose the following selection criteria to guide feature evaluation in this dataset: (1) [Formula: see text] of feature ratios [Formula: see text], (2) standard deviation of feature ratios [Formula: see text], (3) correlation of features [Formula: see text], and (4) [Formula: see text]. Statistically significant correlation coefficients ranged from 0.13 to 0.68 in comparisons between the two mammographic systems tested. Features describing spatial patterns tended to exhibit high correlation coefficients, while intensity- and directionality-based features had comparatively poor correlation. Our proposed robustness metrics may be used to evaluate other datasets, for which different ranges of metric values may be appropriate.
Collapse
Affiliation(s)
- Kayla R Mendel
- University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Hui Li
- University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Li Lan
- University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Cathleen M Cahill
- University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Victoria Rael
- University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Hiroyuki Abe
- University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Maryellen L Giger
- University of Chicago, Department of Radiology, Chicago, Illinois, United States
| |
Collapse
|
41
|
Mass classification of benign and malignant with a new twin support vector machine joint
$${l_{2,1}}$$
l
2
,
1
-norm. INT J MACH LEARN CYB 2017. [DOI: 10.1007/s13042-017-0706-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
42
|
Singh VP, Srivastava S, Srivastava R. Effective mammogram classification based on center symmetric-LBP features in wavelet domain using random forests. Technol Health Care 2017; 25:709-727. [DOI: 10.3233/thc-170851] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
|
43
|
Taghanaki SA, Kawahara J, Miles B, Hamarneh G. Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 145:85-93. [PMID: 28552129 DOI: 10.1016/j.cmpb.2017.04.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Revised: 03/21/2017] [Accepted: 04/12/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Feature reduction is an essential stage in computer aided breast cancer diagnosis systems. Multilayer neural networks can be trained to extract relevant features by encoding high-dimensional data into low-dimensional codes. Optimizing traditional auto-encoders works well only if the initial weights are close to a proper solution. They are also trained to only reduce the mean squared reconstruction error (MRE) between the encoder inputs and the decoder outputs, but do not address the classification error. The goal of the current work is to test the hypothesis that extending traditional auto-encoders (which only minimize reconstruction error) to multi-objective optimization for finding Pareto-optimal solutions provides more discriminative features that will improve classification performance when compared to single-objective and other multi-objective approaches (i.e. scalarized and sequential). METHODS In this paper, we introduce a novel multi-objective optimization of deep auto-encoder networks, in which the auto-encoder optimizes two objectives: MRE and mean classification error (MCE) for Pareto-optimal solutions, rather than just MRE. These two objectives are optimized simultaneously by a non-dominated sorting genetic algorithm. RESULTS We tested our method on 949 X-ray mammograms categorized into 12 classes. The results show that the features identified by the proposed algorithm allow a classification accuracy of up to 98.45%, demonstrating favourable accuracy over the results of state-of-the-art methods reported in the literature. CONCLUSIONS We conclude that adding the classification objective to the traditional auto-encoder objective and optimizing for finding Pareto-optimal solutions, using evolutionary multi-objective optimization, results in producing more discriminative features.
Collapse
Affiliation(s)
| | - Jeremy Kawahara
- Medical Image Analysis Lab, Simon Fraser University, Canada.
| | - Brandon Miles
- Medical Image Analysis Lab, Simon Fraser University, Canada.
| | | |
Collapse
|
44
|
Prabusankarlal KM, Thirumoorthy P, Manavalan R. Classification of breast masses in ultrasound images using self-adaptive differential evolution extreme learning machine and rough set feature selection. J Med Imaging (Bellingham) 2017; 4:024507. [PMID: 28653015 DOI: 10.1117/1.jmi.4.2.024507] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Accepted: 05/25/2017] [Indexed: 11/14/2022] Open
Abstract
A method using rough set feature selection and extreme learning machine (ELM) whose learning strategy and hidden node parameters are optimized by self-adaptive differential evolution (SaDE) algorithm for classification of breast masses is investigated. A pathologically proven database of 140 breast ultrasound images, including 80 benign and 60 malignant, is used for this study. A fast nonlocal means algorithm is applied for speckle noise removal, and multiresolution analysis of undecimated discrete wavelet transform is used for accurate segmentation of breast lesions. A total of 34 features, including 29 textural and five morphological, are applied to a [Formula: see text]-fold cross-validation scheme, in which more relevant features are selected by quick-reduct algorithm, and the breast masses are discriminated into benign or malignant using SaDE-ELM classifier. The diagnosis accuracy of the system is assessed using parameters, such as accuracy (Ac), sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), Matthew's correlation coefficient (MCC), and area ([Formula: see text]) under receiver operating characteristics curve. The performance of the proposed system is also compared with other classifiers, such as support vector machine and ELM. The results indicated that the proposed SaDE algorithm has superior performance with [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] compared to other classifiers.
Collapse
Affiliation(s)
- Kadayanallur Mahadevan Prabusankarlal
- Bharathiar University, Research and Development Centre, Department of Electronics and Instrumentation, Coimbatore, India.,K.S. Rangasamy College of Arts and Science (Autonomous), Department of Electronics and Communication, Tiruchengode, India
| | | | - Radhakrishnan Manavalan
- Arignar Anna Government Arts College, Department of Computer Applications and Information Technology, Villupuram, India
| |
Collapse
|
45
|
Ebrahimpour MK, Mirvaziri H, Sattari-Naeini V. Improving breast cancer classification by dimensional reduction on mammograms. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2017. [DOI: 10.1080/21681163.2017.1326847] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
| | - Hamid Mirvaziri
- Department of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Vahid Sattari-Naeini
- Department of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| |
Collapse
|
46
|
Wang J, Nishikawa RM, Yang Y. Global detection approach for clustered microcalcifications in mammograms using a deep learning network. J Med Imaging (Bellingham) 2017; 4:024501. [PMID: 28466029 DOI: 10.1117/1.jmi.4.2.024501] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2016] [Accepted: 04/06/2017] [Indexed: 11/14/2022] Open
Abstract
In computerized detection of clustered microcalcifications (MCs) from mammograms, the traditional approach is to apply a pattern detector to locate the presence of individual MCs, which are subsequently grouped into clusters. Such an approach is often susceptible to the occurrence of false positives (FPs) caused by local image patterns that resemble MCs. We investigate the feasibility of a direct detection approach to determining whether an image region contains clustered MCs or not. Toward this goal, we develop a deep convolutional neural network (CNN) as the classifier model to which the input consists of a large image window ([Formula: see text] in size). The multiple layers in the CNN classifier are trained to automatically extract image features relevant to MCs at different spatial scales. In the experiments, we demonstrated this approach on a dataset consisting of both screen-film mammograms and full-field digital mammograms. We evaluated the detection performance both on classifying image regions of clustered MCs using a receiver operating characteristic (ROC) analysis and on detecting clustered MCs from full mammograms by a free-response receiver operating characteristic analysis. For comparison, we also considered a recently developed MC detector with FP suppression. In classifying image regions of clustered MCs, the CNN classifier achieved 0.971 in the area under the ROC curve, compared to 0.944 for the MC detector. In detecting clustered MCs from full mammograms, at 90% sensitivity, the CNN classifier obtained an FP rate of 0.69 clusters/image, compared to 1.17 clusters/image by the MC detector. These results indicate that using global image features can be more effective in discriminating clustered MCs from FPs caused by various sources, such as linear structures, thereby providing a more accurate detection of clustered MCs on mammograms.
Collapse
Affiliation(s)
- Juan Wang
- Illinois Institute of Technology, Medical Imaging Research Center, Department of Electrical and Computer Engineering, Chicago, Illinois, United States
| | - Robert M Nishikawa
- University of Pittsburgh, Department of Radiology, Pittsburgh, Pennsylvania, United States
| | - Yongyi Yang
- Illinois Institute of Technology, Medical Imaging Research Center, Department of Electrical and Computer Engineering, Chicago, Illinois, United States
| |
Collapse
|
47
|
Marin Z, Batchelder KA, Toner BC, Guimond L, Gerasimova-Chechkina E, Harrow AR, Arneodo A, Khalil A. Mammographic evidence of microenvironment changes in tumorous breasts. Med Phys 2017; 44:1324-1336. [DOI: 10.1002/mp.12120] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Revised: 11/28/2016] [Accepted: 01/11/2017] [Indexed: 12/15/2022] Open
Affiliation(s)
- Zach Marin
- CompuMAINE Laboratory; Department of Mathematics & Statistics; University of Maine; Orono ME 04469 USA
| | - Kendra A. Batchelder
- CompuMAINE Laboratory; Department of Mathematics & Statistics; University of Maine; Orono ME 04469 USA
| | - Brian C. Toner
- CompuMAINE Laboratory; Department of Mathematics & Statistics; University of Maine; Orono ME 04469 USA
| | - Lyne Guimond
- CompuMAINE Laboratory; Department of Mathematics & Statistics; University of Maine; Orono ME 04469 USA
| | | | - Amy R. Harrow
- Spectrum Medical Group; Eastern Maine Medical Center; Bangor ME 04401 USA
| | - Alain Arneodo
- LOMA; Universite de Bordeaux; CNRS; UMR 5798; 33405 Talence France
| | - Andre Khalil
- CompuMAINE Laboratory; Department of Mathematics & Statistics; University of Maine; Orono ME 04469 USA
| |
Collapse
|
48
|
Albiol A, Corbi A, Albiol F. Automatic intensity windowing of mammographic images based on a perceptual metric. Med Phys 2017; 44:1369-1378. [PMID: 28160525 DOI: 10.1002/mp.12144] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Revised: 01/13/2017] [Accepted: 01/24/2017] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Initial auto-adjustment of the window level WL and width WW applied to mammographic images. The proposed intensity windowing (IW) method is based on the maximization of the mutual information (MI) between a perceptual decomposition of the original 12-bit sources and their screen displayed 8-bit version. Besides zoom, color inversion and panning operations, IW is the most commonly performed task in daily screening and has a direct impact on diagnosis and the time involved in the process. METHODS The authors present a human visual system and perception-based algorithm named GRAIL (Gabor-relying adjustment of image levels). GRAIL initially measures a mammogram's quality based on the MI between the original instance and its Gabor-filtered derivations. From this point on, the algorithm performs an automatic intensity windowing process that outputs the WL/WW that best displays each mammogram for screening. GRAIL starts with the default, high contrast, wide dynamic range 12-bit data, and then maximizes the graphical information presented in ordinary 8-bit displays. Tests have been carried out with several mammogram databases. They comprise correlations and an ANOVA analysis with the manual IW levels established by a group of radiologists. A complete MATLAB implementation of GRAIL is available at https://github.com/TheAnswerIsFortyTwo/GRAIL. RESULTS Auto-leveled images show superior quality both perceptually and objectively compared to their full intensity range and compared to the application of other common methods like global contrast stretching (GCS). The correlations between the human determined intensity values and the ones estimated by our method surpass that of GCS. The ANOVA analysis with the upper intensity thresholds also reveals a similar outcome. GRAIL has also proven to specially perform better with images that contain micro-calcifications and/or foreign X-ray-opaque elements and with healthy BI-RADS A-type mammograms. It can also speed up the initial screening time by a mean of 4.5 s per image. CONCLUSIONS A novel methodology is introduced that enables a quality-driven balancing of the WL/WW of mammographic images. This correction seeks the representation that maximizes the amount of graphical information contained in each image. The presented technique can contribute to the diagnosis and the overall efficiency of the breast screening session by suggesting, at the beginning, an optimal and customized windowing setting for each mammogram.
Collapse
Affiliation(s)
- Alberto Albiol
- iTeam Research Institute, Universitat Politlècnica de Valéncia, València, Spain
| | - Alberto Corbi
- Instituto de Física Corpuscular (IFIC), Consejo Superior de Investigaciones Científicas, Universitat de València, València, Spain
| | - Francisco Albiol
- Instituto de Física Corpuscular (IFIC), Consejo Superior de Investigaciones Científicas, Universitat de València, València, Spain
| |
Collapse
|
49
|
Alamudun F, Yoon HJ, Hudson KB, Morin-Ducote G, Hammond T, Tourassi GD. Fractal analysis of visual search activity for mass detection during mammographic screening. Med Phys 2017; 44:832-846. [PMID: 28079249 DOI: 10.1002/mp.12100] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Revised: 10/18/2016] [Accepted: 12/20/2016] [Indexed: 11/10/2022] Open
Abstract
PURPOSE The objective of this study was to assess the complexity of human visual search activity during mammographic screening using fractal analysis and to investigate its relationship with case and reader characteristics. METHODS The study was performed for the task of mammographic screening with simultaneous viewing of four coordinated breast views as typically done in clinical practice. Eye-tracking data and diagnostic decisions collected for 100 mammographic cases (25 normal, 25 benign, 50 malignant) from 10 readers (three board certified radiologists and seven Radiology residents), formed the corpus for this study. The fractal dimension of the readers' visual scanning pattern was computed with the Minkowski-Bouligand box-counting method and used as a measure of gaze complexity. Individual factor and group-based interaction ANOVA analysis was performed to study the association between fractal dimension, case pathology, breast density, and reader experience level. The consistency of the observed trends depending on gaze data representation was also examined. RESULTS Case pathology, breast density, reader experience level, and individual reader differences are all independent predictors of the complexity of visual scanning pattern when screening for breast cancer. No higher order effects were found to be significant. CONCLUSIONS Fractal characterization of visual search behavior during mammographic screening is dependent on case properties and image reader characteristics.
Collapse
Affiliation(s)
- Folami Alamudun
- Biomedical Sciences, Engineering, and Computing Group, Health Data Sciences Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Hong-Jun Yoon
- Biomedical Sciences, Engineering, and Computing Group, Health Data Sciences Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Kathleen B Hudson
- Department of Radiology, University of Tennessee Medical Center at Knoxville, Knoxville, TN, 37920, USA
| | - Garnetta Morin-Ducote
- Department of Radiology, University of Tennessee Medical Center at Knoxville, Knoxville, TN, 37920, USA
| | - Tracy Hammond
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA, 77843
| | - Georgia D Tourassi
- Biomedical Sciences, Engineering, and Computing Group, Health Data Sciences Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| |
Collapse
|
50
|
Kumar I, H.S. B, Virmani J, Thakur S. A classification framework for prediction of breast density using an ensemble of neural network classifiers. Biocybern Biomed Eng 2017. [DOI: 10.1016/j.bbe.2017.01.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|