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Queiroz KFFDC, de Cássia Fernandes de Lima R. Smart screening system for breast cancer: the use of thermographic images, evolutionary algorithms, and oversampling. Biomed Phys Eng Express 2023; 9:055027. [PMID: 37207632 DOI: 10.1088/2057-1976/acd6fe] [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: 02/17/2023] [Accepted: 05/19/2023] [Indexed: 05/21/2023]
Abstract
Purpose.This study aimed to develop a computer system for automatic detection of thermographic changes indicating breast malignancy risk.Materials and methods. The database contained 233 thermograms of women, including 43 with malignant lesions and 190 with no malignant lesions. Five classifiers were evaluated (k-Nearest Neighbor, Support Vector Machine, Decision Tree, Discriminant Analysis, and Naive Bayes) in combination with oversampling techniques. An attribute selection approach using genetic algorithms was considered. Performance was assessed using accuracy, sensitivity, specificity, AUC, and Kappa statistics.Results. Support vector machines combined with attribute selection by genetic algorithm and ASUWO oversampling obtained the best performance. Attributes were reduced by 41.38%, and accuracy was 95.23%, sensitivity was 93.65%, and specificity was 96.81%. The Kappa index was 0.90, and AUC was 0.99.Conclusion. The feature selection process lowered computational costs and improved diagnostic accuracy. A high-performance system using a new breast imaging modality could positively aid breast cancer screening.
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2
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Holanda AGA, Cortez DEA, Queiroz GFD, Matera JM. Applicability of thermography for cancer diagnosis in small animals. J Therm Biol 2023; 114:103561. [PMID: 37344014 DOI: 10.1016/j.jtherbio.2023.103561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 03/30/2023] [Accepted: 04/04/2023] [Indexed: 06/23/2023]
Abstract
Medical thermography is an imaging test used to monitor skin surface temperature. Although it is not a recent technique, significant advances have been made since the 2000s with the equipment modernization, leading to its popularization. In cancer diagnosis, the application of thermography is supported by the difference in thermal distribution between neoplastic processes and adjacent healthy tissue. The mechanisms involved in heat production by cancer cells include neoangiogenesis, increased metabolic rate, vasodilation, and the release of nitric oxide and pro-inflammatory substances. Currently, thermography has been widely studied in humans as a screening tool for skin and breast cancer, with positive results. In veterinary medicine, the technique has shown promise and has been described for skin and soft tissue tumors in felines, mammary gland tumors, osteosarcoma, mast cell tumors, and perianal tumors in dogs. This review discusses the fundamentals of the technique, monitoring conditions, and the role of thermography as a complementary diagnostic tool for cancer in veterinary medicine, as well as future perspectives for improvement.
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Affiliation(s)
| | | | | | - Julia Maria Matera
- Department of Surgery, Faculty of Veterinary Medicine and Animal Science, University of São Paulo (USP), São Paulo, SP, Brazil
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3
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Pérez-Martín J, Sánchez-Cauce R. Quality analysis of a breast thermal images database. Health Informatics J 2023; 29:14604582231153779. [PMID: 36731024 DOI: 10.1177/14604582231153779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The study and early detection of breast cancer are key for its treatment. We carry out an exhaustive analysis of the most used database for mastology research with infrared images, analyzing the anomalies according to five quality dimensions: completeness, correctness, concordance, plausibility, and currency. We established control queries that looked for these anomalies and that can be used to ensure the quality of the database. Finally, we briefly review the more than 40 papers that use this database and that do not mention any of these anomalies. When analyzing the database, we found 365 anomalies related to personal and clinical data, and thermal images. The errors found in our research may lead to a modification of the results and conclusions made in the articles found in the literature, serve as a basis for improvements in the quality of the database, and help future researchers to work with it.
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Affiliation(s)
- Jorge Pérez-Martín
- Department of Artificial Intelligence, 16757Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain
| | - Raquel Sánchez-Cauce
- Department of Artificial Intelligence, 16757Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain
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4
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A survey on multi-objective hyperparameter optimization algorithms for machine learning. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10359-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
AbstractHyperparameter optimization (HPO) is a necessary step to ensure the best possible performance of Machine Learning (ML) algorithms. Several methods have been developed to perform HPO; most of these are focused on optimizing one performance measure (usually an error-based measure), and the literature on such single-objective HPO problems is vast. Recently, though, algorithms have appeared that focus on optimizing multiple conflicting objectives simultaneously. This article presents a systematic survey of the literature published between 2014 and 2020 on multi-objective HPO algorithms, distinguishing between metaheuristic-based algorithms, metamodel-based algorithms and approaches using a mixture of both. We also discuss the quality metrics used to compare multi-objective HPO procedures and present future research directions.
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5
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Basurto-Hurtado JA, Cruz-Albarran IA, Toledano-Ayala M, Ibarra-Manzano MA, Morales-Hernandez LA, Perez-Ramirez CA. Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms. Cancers (Basel) 2022; 14:3442. [PMID: 35884503 PMCID: PMC9322973 DOI: 10.3390/cancers14143442] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 07/02/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023] Open
Abstract
Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.
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Affiliation(s)
- Jesus A. Basurto-Hurtado
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
| | - Irving A. Cruz-Albarran
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
| | - Manuel Toledano-Ayala
- División de Investigación y Posgrado de la Facultad de Ingeniería (DIPFI), Universidad Autónoma de Querétaro, Cerro de las Campanas S/N Las Campanas, Santiago de Querétaro 76010, Mexico;
| | - Mario Alberto Ibarra-Manzano
- Laboratorio de Procesamiento Digital de Señales, Departamento de Ingeniería Electrónica, Division de Ingenierias Campus Irapuato-Salamanca (DICIS), Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico;
| | - Luis A. Morales-Hernandez
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
| | - Carlos A. Perez-Ramirez
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
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6
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Fernandez-Basso C, Gutiérrez-Batista K, Morcillo-Jiménez R, Vila MA, Martin-Bautista MJ. A fuzzy-based medical system for pattern mining in a distributed environment: Application to diagnostic and co-morbidity. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.108870] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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7
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Pena FB, Crabi D, Izidoro SC, Rodrigues ÉO, Bernardes G. Machine learning applied to emerald gemstone grading: framework proposal and creation of a public dataset. Pattern Anal Appl 2021. [DOI: 10.1007/s10044-021-01041-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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8
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Resmini R, Faria da Silva L, Medeiros PRT, Araujo AS, Muchaluat-Saade DC, Conci A. A hybrid methodology for breast screening and cancer diagnosis using thermography. Comput Biol Med 2021; 135:104553. [PMID: 34246159 DOI: 10.1016/j.compbiomed.2021.104553] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 05/02/2021] [Accepted: 06/02/2021] [Indexed: 12/13/2022]
Abstract
Breast cancer is the second most common cancer in the world. Early diagnosis and treatment increase the patient's chances of healing. The temperature of cancerous tissues is generally different from that of healthy neighboring tissues, making thermography an option to be considered in the fight against cancer because it does not use ionizing radiation, venous access, or any other invasive process, presenting no damage or risk to the patient. In this paper, we propose a hybrid computational method using the Dynamic Infrared Thermography (DIT) and Static Infrared Thermography (SIT) for abnormality screening and diagnosis of malignant tumor (cancer), applying supervised and unsupervised machine learning techniques. We use the area under receiver operating characteristic curve, sensitivity, specificity, and accuracy as performance measures to compare the hybrid methodology with previous work in the literature. The K-Star classifier achieved accuracy of 99% in the screening phase using DIT images. The Support Vector Machines (SVM) classifier applied on SIT images yielded accuracy of 95% in the diagnosis of cancer. The results confirm the potential of the proposed approaches for screening and diagnosis of breast cancer.
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Affiliation(s)
- Roger Resmini
- Institute of Exact and Natural Sciences, Federal University of Rondonópolis, Cidade Universitária, Rondonópolis, MT, 78736-900, Brazil; Visual Lab, Institute of Computing, Fluminense Federal University, Av. Gal. Milton Tavares de Souza, S/N - Niterói, RJ, 24210-346, Brazil.
| | - Lincoln Faria da Silva
- Advanced Research Medical Laboratory, Departament of Information Technology and Education in Health, Faculty of Medical Sciences, State University of Rio de Janeiro, R. Professor Manuel de Abreu, 444, Rio de Janeiro, RJ, 20550-170, Brazil.
| | - Petrucio R T Medeiros
- Mídiacom Lab, Institute of Computing, Fluminense Federal University, R. Passo da Pátria 156, Niterói, RJ, 24210-240, Brazil.
| | - Adriel S Araujo
- Visual Lab, Institute of Computing, Fluminense Federal University, Av. Gal. Milton Tavares de Souza, S/N - Niterói, RJ, 24210-346, Brazil.
| | - Débora C Muchaluat-Saade
- Mídiacom Lab, Institute of Computing, Fluminense Federal University, R. Passo da Pátria 156, Niterói, RJ, 24210-240, Brazil.
| | - Aura Conci
- Visual Lab, Institute of Computing, Fluminense Federal University, Av. Gal. Milton Tavares de Souza, S/N - Niterói, RJ, 24210-346, Brazil.
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9
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Meta-Analysis and Systematic Review of the Application of Machine Learning Classifiers in Biomedical Applications of Infrared Thermography. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11020842] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Atypical body temperature values can be an indication of abnormal physiological processes associated with several health conditions. Infrared thermal (IRT) imaging is an innocuous imaging modality capable of capturing the natural thermal radiation emitted by the skin surface, which is connected to physiology-related pathological states. The implementation of artificial intelligence (AI) methods for interpretation of thermal data can be an interesting solution to supply a second opinion to physicians in a diagnostic/therapeutic assessment scenario. The aim of this work was to perform a systematic review and meta-analysis concerning different biomedical thermal applications in conjunction with machine learning strategies. The bibliographic search yielded 68 records for a qualitative synthesis and 34 for quantitative analysis. The results show potential for the implementation of IRT imaging with AI, but more work is needed to retrieve significant features and improve classification metrics.
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10
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Zuluaga-Gomez J, Al Masry Z, Benaggoune K, Meraghni S, Zerhouni N. A CNN-based methodology for breast cancer diagnosis using thermal images. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2020. [DOI: 10.1080/21681163.2020.1824685] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- J. Zuluaga-Gomez
- FEMTO-ST Institute, Univ. Bourgogne Franche-Comt, CNRS, ENSMM, Besancon, France
- Automatic Speech Recognition Research Group, Idiap Research Institute, Martigny, Switzerland
- Ecole Polytechnique Federale De Lausanne (EPFL), Switzerland
| | - Z. Al Masry
- FEMTO-ST Institute, Univ. Bourgogne Franche-Comt, CNRS, ENSMM, Besancon, France
| | - K. Benaggoune
- Laboratory of Automation and Production Engineering, Batna University, Batna, Algeria
| | - S. Meraghni
- LINFI Laboratory, University of Biskra, Biskra, Algeria
| | - N. Zerhouni
- FEMTO-ST Institute, Univ. Bourgogne Franche-Comt, CNRS, ENSMM, Besancon, France
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11
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A novel method for predicting the progression rate of ALS disease based on automatic generation of probabilistic causal chains. Artif Intell Med 2020; 107:101879. [PMID: 32828438 DOI: 10.1016/j.artmed.2020.101879] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 04/17/2020] [Accepted: 05/12/2020] [Indexed: 01/22/2023]
Abstract
Causal discovery is considered as a major concept in biomedical informatics contributing to diagnosis, therapy, and prognosis of diseases. Probabilistic causality approaches in epidemiology and medicine is a common method for finding relationships between pathogen and disease, environment and disease, and adverse events and drugs. Bayesian Network (BN) is one of the common approaches for probabilistic causality, which is widely used in health-care and biomedical science. Since in many biomedical applications we deal with temporal dataset, the temporal extension of BNs called Dynamic Bayesian network (DBN) is used for such applications. DBNs define probabilistic relationships between parameters in consecutive time points in the form of a graph and have been successfully used in many biomedical applications. In this paper, a novel method was introduced for finding probabilistic causal chains from a temporal dataset with the help of entropy and causal tendency measures. In this method, first, Causal Features Dependency (CFD) matrix is created on the basis of parameters changes in consecutive events of a phenomenon, and then the probabilistic causal graph is constructed from this matrix based on entropy criteria. At the next step, a set of probabilistic causal chains of the corresponding causal graph is constructed by a novel polynomial-time heuristic. Finally, the causal chains are used for predicting the future trend of the phenomenon. The proposed model was applied to the Pooled Resource Open-Access Clinical Trials (PRO-ACT) dataset related to Amyotrophic Lateral Sclerosis (ALS) disease, in order to predict the progression rate of this disease. The results of comparison with Bayesian tree, random forest, support vector regression, linear regression, and multivariate regression show that the proposed algorithm can compete with these methods and in some cases outperforms other algorithms. This study revealed that probabilistic causality is an appropriate approach for predicting the future states of chronic diseases with unknown cause.
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12
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A Computational Method to Assist the Diagnosis of Breast Disease Using Dynamic Thermography. SENSORS 2020; 20:s20143866. [PMID: 32664410 PMCID: PMC7412156 DOI: 10.3390/s20143866] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 06/24/2020] [Accepted: 07/06/2020] [Indexed: 11/16/2022]
Abstract
Breast cancer has been the second leading cause of cancer death among women. New techniques to enhance early diagnosis are very important to improve cure rates. This paper proposes and evaluates an image analysis method to automatically detect patients with breast benign and malignant changes (tumors). Such method explores the difference of Dynamic Infrared Thermography (DIT) patterns observed in patients’ skin. After obtaining the sequential DIT images of each patient, their temperature arrays are computed and new images in gray scale are generated. Then the regions of interest (ROIs) of those images are segmented and, from them, arrays of the ROI temperature are computed. Features are extracted from the arrays, such as the ones based on statistical, clustering, histogram comparison, fractal geometry, diversity indices and spatial statistics. Time series that are broken down into subsets of different cardinalities are generated from such features. Automatic feature selection methods are applied and used in the Support Vector Machine (SVM) classifier. In our tests, using a dataset of 68 images, 100% accuracy was achieved.
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13
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Breast cancer diagnosis using thermography and convolutional neural networks. Med Hypotheses 2020; 137:109542. [DOI: 10.1016/j.mehy.2019.109542] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 12/26/2019] [Indexed: 11/19/2022]
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14
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Kirimtat A, Krejcar O, Selamat A, Herrera-Viedma E. FLIR vs SEEK thermal cameras in biomedicine: comparative diagnosis through infrared thermography. BMC Bioinformatics 2020; 21:88. [PMID: 32164529 PMCID: PMC7069161 DOI: 10.1186/s12859-020-3355-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND In biomedicine, infrared thermography is the most promising technique among other conventional methods for revealing the differences in skin temperature, resulting from the irregular temperature dispersion, which is the significant signaling of diseases and disorders in human body. Given the process of detecting emitted thermal radiation of human body temperature by infrared imaging, we, in this study, present the current utility of thermal camera models namely FLIR and SEEK in biomedical applications as an extension of our previous article. RESULTS The most significant result is the differences between image qualities of the thermograms captured by thermal camera models. In other words, the image quality of the thermal images in FLIR One is higher than SEEK Compact PRO. However, the thermal images of FLIR One are noisier than SEEK Compact PRO since the thermal resolution of FLIR One is 160 × 120 while it is 320 × 240 in SEEK Compact PRO. CONCLUSION Detecting and revealing the inhomogeneous temperature distribution on the injured toe of the subject, we, in this paper, analyzed the imaging results of two different smartphone-based thermal camera models by making comparison among various thermograms. Utilizing the feasibility of the proposed method for faster and comparative diagnosis in biomedical problems is the main contribution of this study.
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Affiliation(s)
- Ayca Kirimtat
- Faculty of Informatics and Management, Center for Basic and Applied Research, University of Hradec Kralove, Rokitanskeho 62, 500 03 Hradec Kralove, Czech Republic
| | - Ondrej Krejcar
- Faculty of Informatics and Management, Center for Basic and Applied Research, University of Hradec Kralove, Rokitanskeho 62, 500 03 Hradec Kralove, Czech Republic
| | - Ali Selamat
- Faculty of Informatics and Management, Center for Basic and Applied Research, University of Hradec Kralove, Rokitanskeho 62, 500 03 Hradec Kralove, Czech Republic
- Malaysia Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra, Kuala Lumpur, 54100 Malaysia
- Digital Cities Research Institute, Multimedia University, Persiaran Multimedia, Cyberjaya, 63100 Malaysia
- Media and Games Center of Excellence (MagicX) Universiti Teknologi Malaysia & School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, 81310 Johor Malaysia
| | - Enrique Herrera-Viedma
- Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, 18071 Granada, Spain
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
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15
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Zuluaga-Gomez J, Zerhouni N, Al Masry Z, Devalland C, Varnier C. A survey of breast cancer screening techniques: thermography and electrical impedance tomography. J Med Eng Technol 2019; 43:305-322. [DOI: 10.1080/03091902.2019.1664672] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- J. Zuluaga-Gomez
- FEMTO-ST Institute, University Bourgogne Franche-Comté, CNRS, ENSMM, Besançon, France
- Department of Electrical Engineering, University of Oviedo, Gijon, Spain
- Universidad Autonoma Del Caribe, Barranquilla, Colombia
| | - N. Zerhouni
- FEMTO-ST Institute, University Bourgogne Franche-Comté, CNRS, ENSMM, Besançon, France
| | - Z. Al Masry
- FEMTO-ST Institute, University Bourgogne Franche-Comté, CNRS, ENSMM, Besançon, France
| | - C. Devalland
- Department of Pathology, Hospital Nord Franche-Comte, Belfort, France
| | - C. Varnier
- FEMTO-ST Institute, University Bourgogne Franche-Comté, CNRS, ENSMM, Besançon, France
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Wang Z, Xin J, Huang Y, Li C, Xu L, Li Y, Zhang H, Gu H, Qian W. A similarity measure method combining location feature for mammogram retrieval. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2018; 26:553-571. [PMID: 29865106 DOI: 10.3233/xst-18374] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
BACKGROUND Breast cancer, the most common malignancy among women, has a high mortality rate in clinical practice. Early detection, diagnosis and treatment can reduce the mortalities of breast cancer greatly. The method of mammogram retrieval can help doctors to find the early breast lesions effectively and determine a reasonable feature set for image similarity measure. This will improve the accuracy effectively for mammogram retrieval. METHODS This paper proposes a similarity measure method combining location feature for mammogram retrieval. Firstly, the images are pre-processed, the regions of interest are detected and the lesions are segmented in order to get the center point and radius of the lesions. Then, the method, namely Coherent Point Drift, is used for image registration with the pre-defined standard image. The center point and radius of the lesions after registration are obtained and the standard location feature of the image is constructed. This standard location feature can help figure out the location similarity between the image pair from the query image to each dataset image in the database. Next, the content feature of the image is extracted, including the Histogram of Oriented Gradients, the Edge Direction Histogram, the Local Binary Pattern and the Gray Level Histogram, and the image pair content similarity can be calculated using the Earth Mover's Distance. Finally, the location similarity and content similarity are fused to form the image fusion similarity, and the specified number of the most similar images can be returned according to it. RESULTS In the experiment, 440 mammograms, which are from Chinese women in Northeast China, are used as the database. When fusing 40% lesion location feature similarity and 60% content feature similarity, the results have obvious advantages. At this time, precision is 0.83, recall is 0.76, comprehensive indicator is 0.79, satisfaction is 96.0%, mean is 4.2 and variance is 17.7. CONCLUSIONS The results show that the precision and recall of this method have obvious advantage, compared with the content-based image retrieval.
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Affiliation(s)
- Zhiqiong Wang
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, China
| | - Junchang Xin
- School of Computer Science and Engineering, Key Laboratory of Big Data Management and Analytics (Liaoning Province), Northeastern University, China
| | - Yukun Huang
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, China
| | - Chen Li
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, China
| | - Ling Xu
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, China
| | - Yang Li
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, China
| | - Hao Zhang
- Breast Disease and Reconstruction Center, Breast Cancer Key Lab of Dalian, the Second Hospital of Dalian Medical University, China
| | - Huizi Gu
- Department of Internal Neurology, the Second Hospital of Dalian Medical University, China
| | - Wei Qian
- College of Engineering, University of Texas at El Paso, USA
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17
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The role of malignant tissue on the thermal distribution of cancerous breast. J Theor Biol 2017; 426:152-161. [DOI: 10.1016/j.jtbi.2017.05.031] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 05/18/2017] [Accepted: 05/23/2017] [Indexed: 11/20/2022]
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