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Mishra J, Kumar B, Targhotra M, Sahoo PK. Advanced and futuristic approaches for breast cancer diagnosis. FUTURE JOURNAL OF PHARMACEUTICAL SCIENCES 2020. [DOI: 10.1186/s43094-020-00113-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Breast cancer is the most frequent cancer and one of the most common causes of death in women, impacting almost 2 million women each year. Tenacity or perseverance of breast cancer in women is very high these days with an extensive increasing rate of 3 to 5% every year. Along with hurdles faced during treatment of breast tumor, one of the crucial causes of delay in treatment is invasive and poor diagnostic techniques for breast cancer hence the early diagnosis of breast tumors will help us to improve its management and treatment in the initial stage.
Main body
Present review aims to explore diagnostic techniques for breast cancer that are currently being used, recent advancements that aids in prior detection and evaluation and are extensively focused on techniques that are going to be future of breast cancer detection with better efficiency and lesser pain to patients so that it helps to a physician to prevent delay in treatment of cancer. Here, we have discussed mammography and its advanced forms that are the need of current era, techniques involving radiation such as radionuclide methods, the potential of nanotechnology by using nanoparticle in breast cancer, and how the new inventions such as breath biopsy, and X-ray diffraction of hair can simply use as a prominent method in breast cancer early and easy detection tool.
Conclusion
It is observed significantly that advancement in detection techniques is helping in early diagnosis of breast cancer; however, we have to also focus on techniques that will improve the future of cancer diagnosis in like optical imaging and HER2 testing.
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Yousefi B, Akbari H, Maldague XP. Detecting Vasodilation as Potential Diagnostic Biomarker in Breast Cancer Using Deep Learning-Driven Thermomics. BIOSENSORS 2020; 10:E164. [PMID: 33142939 PMCID: PMC7693609 DOI: 10.3390/bios10110164] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 10/29/2020] [Accepted: 10/30/2020] [Indexed: 12/11/2022]
Abstract
Breast cancer is the most common cancer in women. Early diagnosis improves outcome and survival, which is the cornerstone of breast cancer treatment. Thermography has been utilized as a complementary diagnostic technique in breast cancer detection. Artificial intelligence (AI) has the capacity to capture and analyze the entire concealed information in thermography. In this study, we propose a method to potentially detect the immunohistochemical response to breast cancer by finding thermal heterogeneous patterns in the targeted area. In this study for breast cancer screening 208 subjects participated and normal and abnormal (diagnosed by mammography or clinical diagnosis) conditions were analyzed. High-dimensional deep thermomic features were extracted from the ResNet-50 pre-trained model from low-rank thermal matrix approximation using sparse principal component analysis. Then, a sparse deep autoencoder designed and trained for such data decreases the dimensionality to 16 latent space thermomic features. A random forest model was used to classify the participants. The proposed method preserves thermal heterogeneity, which leads to successful classification between normal and abnormal subjects with an accuracy of 78.16% (73.3-81.07%). By non-invasively capturing a thermal map of the entire tumor, the proposed method can assist in screening and diagnosing this malignancy. These thermal signatures may preoperatively stratify the patients for personalized treatment planning and potentially monitor the patients during treatment.
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Affiliation(s)
- Bardia Yousefi
- Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Hamed Akbari
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Xavier P.V. Maldague
- Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada
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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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54
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A Fast Image Thresholding Algorithm for Infrared Images Based on Histogram Approximation and Circuit Theory. ALGORITHMS 2020. [DOI: 10.3390/a13090207] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Image thresholding is one of the fastest and most effective methods of detecting objects in infrared images. This paper proposes an infrared image thresholding method based on the functional approximation of the histogram. The one-dimensional histogram of the image is approximated to the transient response of a first-order linear circuit. The threshold value for the image segmentation is formulated using combinational analogues of standard operators and principles from the concept of the transient behavior of the first-order linear circuit. The proposed method is tested on infrared images gathered from the standard databases and the experimental results are compared with the existing state-of-the-art infrared image thresholding methods. We realized through the experimental results that our method is well suited to perform infrared image thresholding.
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Mewada HK, Patel AV, Hassaballah M, Alkinani MH, Mahant K. Spectral-Spatial Features Integrated Convolution Neural Network for Breast Cancer Classification. SENSORS (BASEL, SWITZERLAND) 2020; 20:E4747. [PMID: 32842640 PMCID: PMC7506633 DOI: 10.3390/s20174747] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 08/17/2020] [Accepted: 08/19/2020] [Indexed: 12/11/2022]
Abstract
Cancer identification and classification from histopathological images of the breast depends greatly on experts, and computer-aided diagnosis can play an important role in disagreement of experts. This automatic process has increased the accuracy of the classification at a reduced cost. The advancement in Convolution Neural Network (CNN) structure has outperformed the traditional approaches in biomedical imaging applications. One of the limiting factors of CNN is it uses spatial image features only for classification. The spectral features from the transform domain have equivalent importance in the complex image classification algorithm. This paper proposes a new CNN structure to classify the histopathological cancer images based on integrating the spectral features obtained using a multi-resolution wavelet transform with the spatial features of CNN. In addition, batch normalization process is used after every layer in the convolution network to improve the poor convergence problem of CNN and the deep layers of CNN are trained with spectral-spatial features. The proposed structure is tested on malignant histology images of the breast for both binary and multi-class classification of tissue using the BreaKHis Dataset and the Breast Cancer Classification Challenge 2015 Datasest. Experimental results show that the combination of spectral-spatial features improves classification accuracy of the CNN network and requires less training parameters in comparison with the well known models (i.e., VGG16 and ALEXNET). The proposed structure achieves an average accuracy of 97.58% and 97.45% with 7.6 million training parameters on both datasets, respectively.
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Affiliation(s)
- Hiren K Mewada
- Electrical Engineering Department, Prince Mohammad Bin Fahd University, Al Khobar 31952, Saudi Arabia;
| | - Amit V Patel
- CHARUSAT Space Research and Technology Center, Charotar University of Science and Technology, Changa, Gujarat 388421, India; (A.V.P.); (K.M.)
| | - Mahmoud Hassaballah
- Department of Computer Science, Faculty of Computers and Information, South Valley University, Qena 83523, Egypt
| | - Monagi H. Alkinani
- Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia;
| | - Keyur Mahant
- CHARUSAT Space Research and Technology Center, Charotar University of Science and Technology, Changa, Gujarat 388421, India; (A.V.P.); (K.M.)
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Taheri-Garavand A, Nasiri A, Banan A, Zhang YD. Smart deep learning-based approach for non-destructive freshness diagnosis of common carp fish. J FOOD ENG 2020. [DOI: 10.1016/j.jfoodeng.2020.109930] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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57
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Hakim A, Awale RN. Thermal Imaging - An Emerging Modality for Breast Cancer Detection: A Comprehensive Review. J Med Syst 2020; 44:136. [PMID: 32613403 DOI: 10.1007/s10916-020-01581-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 04/27/2020] [Indexed: 02/07/2023]
Abstract
Breast cancer is not preventable. To reduce the death rate and improve the survival chances of breast cancer patients, early and accurate detection is the only panacea. Delay in diagnosis of this disease causes 60% of deaths. Thermal imaging is a low-risk modality for early breast cancer decision making without injecting any form of energy into the human body. Thermography as a screening tool was first introduced and well accepted in 1956. However, a study in 1977 found that it lagged behind other screening tools and is subjective. Soon after, its use was discontinued. This review discusses various screening tools used to detect breast cancer with a focus on thermography along with their advantages and shortcomings. With the maturation of thermography equipment and technological advances, this technique is emerging and has become the refocus of many biomedical researchers across the globe in the past decade. This study dispenses an exhaustive review of the work done related to interpretation of breast thermal variations and confers the discipline, frameworks, and methodologies used by different authors to diagnose breast cancer. Different performance metrics like accuracy, specificity, and sensitivity have also been examined. This paper outlines the most pressing research gaps for future work to improvise the accuracy of results for diagnosis of breast abnormalities using image processing tools, mathematical modelling and artificial intelligence. However, supplementary research is needed to affirm the potential of this technology for predicting breast cancer risk effectively. Altogether, our findings inform that it is a promising research problem and a potential solution for early detection of breast cancer in younger women.
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Affiliation(s)
- Aayesha Hakim
- Veermata Jijabai Technological Institute, Mumbai, India.
| | - R N Awale
- Veermata Jijabai Technological Institute, Mumbai, India
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Tong H, Wu Y, Yan Y, Dong Y, Guan X, Liu Y, Lu Z. No association between abortion and risk of breast cancer among nulliparous women: Evidence from a meta-analysis. Medicine (Baltimore) 2020; 99:e20251. [PMID: 32384520 PMCID: PMC7220471 DOI: 10.1097/md.0000000000020251] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Various epidemiological studies have demonstrated the association between abortion and risk of breast cancer among nulliparous women; however, results remain inconclusive. This meta-analysis assessed the association based on previous studies. METHODS PubMed, EMBase, China National Knowledge Infrastructure, Chongqing VIP, and Wanfang databases were searched for relevant articles until February 2018. In this meta-analysis, fixed-effects models were used to estimate the combined effect size and the corresponding 95% confidence interval (CI). All statistical data were analyzed using STATA 12.0. RESULTS A total of 14 articles consisting of 6 cohort studies and 8 case-control studies were included in this review. All articles were of high quality, as determined based on the Newcastle Ottawa Scale assessment. The combined risk ratio (RR) indicated no significant association between abortion and breast cancer among nulliparous women (RR = 1.023, 95%CI = 0.938-1.117; Z = 0.51, P = .607). Subgroup analyses revealed no significant associations between risk of breast cancer and induced abortion or between risk of breast cancer and spontaneous abortion (SA) among nulliparous women (RR = 1.008, 95% CI = 0.909-1.118 and RR = 1.062, 95%CI = 0.902-1.250, respectively). Neither 1 nor >2 abortions increased the risk of breast cancer among nulliparous women. Sensitivity analysis showed that our results were reliable and stable. CONCLUSION Current evidence based on epidemiological studies showed no association between abortion and risk of breast cancer among nulliparous women.
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Affiliation(s)
- Huazhang Tong
- Oncology Department, Jiangxi Provincial People's Hospital Affiliated to Nanchang University
| | - Yifan Wu
- Medical College of Nanchang University
| | - Yin Yan
- Department of rehabilitation medicine, the First Affiliated Hospital of Nanchang University
| | - Yonghai Dong
- Jiangxi Provincial Center for Disease Control and Prevention
| | - Xihong Guan
- Remote Medical Consultation Center, Jiangxi Provincial People's Hospital Affiliated to Nanchang University
| | - Yun Liu
- Cadre Wards of Neurology Medicine, Jiangxi Provincial People's Hospital Affiliated to Nanchang University
| | - ZhiHui Lu
- Oncology Department, the First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
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59
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Kleine TS, Glass RS, Lichtenberger DL, Mackay ME, Char K, Norwood RA, Pyun J. 100th Anniversary of Macromolecular Science Viewpoint: High Refractive Index Polymers from Elemental Sulfur for Infrared Thermal Imaging and Optics. ACS Macro Lett 2020; 9:245-259. [PMID: 35638673 DOI: 10.1021/acsmacrolett.9b00948] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Optical technologies in the midwave and long wave infrared spectrum (MWIR, LWIR) are important systems for high resolution thermal imaging in near, or complete darkness. While IR thermal imaging has been extensively utilized in the defense sector, application of this technology is being driven toward emerging consumer markets and transportation. In this viewpoint, we review the field of IR thermal imaging and discuss the emerging use of synthetic organic and hybrid polymers as novel IR transmissive materials for this application. In particular, we review the critical role of elemental sulfur as a novel feedstock to prepare high refractive index polymers via inverse vulcanization and discuss the fundamental chemical insights required to impart improved IR transparency into these polymeric materials.
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Affiliation(s)
- Tristan S. Kleine
- Department of Chemistry and Biochemistry, The University of Arizona, Tucson, Arizona 85721, United States
| | - Richard S. Glass
- Department of Chemistry and Biochemistry, The University of Arizona, Tucson, Arizona 85721, United States
| | - Dennis L. Lichtenberger
- Department of Chemistry and Biochemistry, The University of Arizona, Tucson, Arizona 85721, United States
| | - Michael E. Mackay
- Department of Materials Science & Engineering, Department of Chemical Engineering, University of Delaware, Newark, Delaware 19711, United States
| | - Kookheon Char
- School of Chemical and Biological Engineering, Seoul 151-744, Republic of Korea
| | - Robert A. Norwood
- Wyant College of Optical Sciences, The University of Arizona, Tucson, Arizona 85721, United States
| | - Jeffrey Pyun
- Department of Chemistry and Biochemistry, The University of Arizona, Tucson, Arizona 85721, United States
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60
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Pauk J, Ihnatouski M, Wasilewska A. Detection of inflammation from finger temperature profile in rheumatoid arthritis. Med Biol Eng Comput 2019; 57:2629-2639. [PMID: 31679125 DOI: 10.1007/s11517-019-02055-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Accepted: 09/30/2019] [Indexed: 10/25/2022]
Abstract
Rheumatoid arthritis (RA) is a chronic inflammatory tissue disease that leads to cartilage, bone, and periarticular tissue damage. This study aimed to investigate whether the use of infrared thermography and measurement of temperature profiles along the hand fingers could detect the inflammation and improve the diagnostic accuracy of the cold provocation test (0 °C for 5 s) and rewarming test (23 °C for180 s) in RA patients. Thirty RA patients (mean age = 49.5 years, standard deviation = 13.0 years) and 22 controls (mean age = 49.8 years, standard deviation = 7.5 years) were studied. Outcomes were the minimal and maximal: baseline temperature (T1), the temperature post-cooling (T2), the temperature post-rewarming (T3), and the Tmax-Tmin along the axis of each finger. The statistical significance was observed for the thumb, index finger, middle finger, and ring finger post-cooling and post-rewarming. Receiver operating characteristics (ROC) analysis to distinguish between the two groups revealed that for the thumb, index finger, middle finger, and ring finger, the area under the ROC curve was statistically significantly (p < 0.05) post-cooling. The cold provocation test used in this study discriminates between RA patients and controls and detects an inflammation in RA patients by the measurement of temperature profiles along the fingers using an infrared camera. Graphical abstract.
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Affiliation(s)
- J Pauk
- Faculty of Mechanical Engineering, Bialystok University of Technology, Wiejska 45C, 15-351, Bialystok, Poland.
| | - M Ihnatouski
- Yanka Kupala State University of Grodno, Elizy Azeska 22, Grodno, Belarus
| | - A Wasilewska
- Faculty of Mechanical Engineering, Bialystok University of Technology, Wiejska 45C, 15-351, Bialystok, Poland
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61
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A Novel Bio-Inspired Method for Early Diagnosis of Breast Cancer through Mammographic Image Analysis. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9214492] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Breast cancer is a current problem that causes the death of many women. In this work, we test meta-heuristics applied to the segmentation of mammographic images. Traditionally, the application of these algorithms has a direct relationship with optimization problems; however, in this study, its implementation is oriented to the segmentation of mammograms using the Dunn index as an optimization function, and the grey levels to represent each individual. The update of grey levels during the process results in the maximization of the Dunn’s index function; the higher the index, the better the segmentation will be. The results showed a lower error rate using these meta-heuristics for segmentation compared to a well-adopted classical approach known as the Otsu method.
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62
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Iqbal HT, Majeed B, Khan U, Bin Altaf MA. An Infrared High classification Accuracy Hand-held Machine Learning based Breast-Cancer Detection System. 2019 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE (BIOCAS) 2019. [DOI: 10.1109/biocas.2019.8918687] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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63
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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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64
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Popa D, Ali SZ, Hopper R, Dai Y, Udrea F. Smart CMOS mid-infrared sensor array. OPTICS LETTERS 2019; 44:4111-4114. [PMID: 31465341 DOI: 10.1364/ol.44.004111] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 07/15/2019] [Indexed: 05/26/2023]
Abstract
We present a novel single-chip thermopile sensor array for mid-infrared room temperature imaging. The array is fabricated on a single complementary metal-oxide-semiconductor (CMOS) dielectric membrane, composed of single-crystal silicon (Si) p+ and n+ elements, and standard CMOS tungsten metal layers for thermopile cold junction heatsinking, significantly reducing the chip size and simplifying its processing. We demonstrate a 16×16 pixel device with 34 V/W responsivity and enhanced optical absorption in the 8-14 μm waveband, with a suitable performance for gesture recognition and people-counting applications. Our simple, low-cost sensor is an attractive on-chip array for a variety of applications in the mid-infrared spectral region.
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Abstract
. Sexual assault can cause great societal damage, with negative socio-economic, mental, sexual, physical and reproductive consequences. According to the Eurostat, the number of crimes increased in the European Union between 2008 and 2016. However, despite the increase in security tools such as cameras, it is usually difficult to know if an individual is subject to an assault based on his or her posture. Hand gestures are seen by many as the natural means of nonverbal communication when interacting with a computer, and a considerable amount of research has been performed. In addition, the identifiable hand placement characteristics provided by modern inexpensive commercial depth cameras can be used in a variety of gesture recognition-based systems, particularly for human-machine interactions. This paper introduces a novel gesture alert system that uses a combination of Convolution Neural Networks (CNNs). The overall system can be subdivided into three main parts: firstly, the human detection in the image using a pretrained “You Only Look Once (YOLO)” method, which extracts the related bounding boxes containing his/her hands; secondly, the gesture detection/classification stage, which processes the bounding box images; and thirdly, we introduced a module called “counterGesture”, which triggers the alert.
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66
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Pauk J, Wasilewska A, Ihnatouski M. Infrared Thermography Sensor for Disease Activity Detection in Rheumatoid Arthritis Patients. SENSORS (BASEL, SWITZERLAND) 2019; 19:E3444. [PMID: 31394720 PMCID: PMC6720753 DOI: 10.3390/s19163444] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 08/02/2019] [Accepted: 08/04/2019] [Indexed: 12/12/2022]
Abstract
A recent review of thermography studies in rheumatoid arthritis shows limited data about disease activity and mostly focuses on differences between the thermography of rheumatoid arthritis patients and typical subjects. A retrospective study compared patients with high disease activity (n = 50), moderate disease activity (n = 16), and healthy participants (n = 42), taking into account demographic, clinical, laboratory, and thermography parameters. We applied an infrared thermography sensor and a fingers examination protocol. Outcomes included the mean temperature of five fingers of a hand: In static, post-cooling, post-rewarming, the total change in mean temperature of fingers due to cold provocation, the total change in mean temperature of fingers due to rewarming, the area under the cooling curve, the area under the heating curve, the difference between the area under the rewarming and the cooling curve, and temperature intensity distribution maps. For patients with high disease activity, a lower area under the heating curve and a lower difference between the area under the rewarming curve and the cooling curve were observed, as well as a smaller total change in mean temperature due to rewarming, compared to patients with moderate disease activity (p < 0.05). Our study findings could be helpful in patients with an equivocal clinical examination.
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Affiliation(s)
- Jolanta Pauk
- Mechanical Engineering Department, Automatics and Robotics Faculty, Bialystok University of Technology, Wiejska 45C, 15-351 Bialystok, Poland.
| | - Agnieszka Wasilewska
- Mechanical Engineering Department, Automatics and Robotics Faculty, Bialystok University of Technology, Wiejska 45C, 15-351 Bialystok, Poland
| | - Mikhail Ihnatouski
- Scientific and Research Department, Yanka Kupala State University of Grodno, Elizy Azeska 22, 230023 Grodno, Belarus
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67
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Waheed KB, Hassan MZU, Hassan DA, Shamrani AAGA, Bassam MA, Elbyali AA, Shams TM, Demiati ZA, Arulanatham ZJ. Breast cancers missed during screening in a tertiary-care hospital mammography facility. Ann Saudi Med 2019; 39:236-243. [PMID: 31381361 PMCID: PMC6838646 DOI: 10.5144/0256-4947.2019.236] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Breast cancer is the most common cancer in females worldwide. Screening with mammography for early breast cancer detection is standard community practice in many countries. OBJECTIVE Identify causes of missed breast cancers during screening. DESIGN Retrospective, observational. SETTING Department of radiology at a tertiary-care hospital mammographic screening facility. PATIENTS AND METHODS All women who came with initial negative screens from July 2015 to July 2018 were retrospectively reviewed and followed-up for their second or subsequent mammographic screening. Missed breast cancer was defined as a cancer that was detected on a subsequent mammogram with an initial negative screen. Mammograms were interpreted by two radiologists as per BIRADS (Breast Imaging Reporting and Data System) lexicon. Causes of missed breast cancers were categorized as imaging acquisition (IA), imaging feature (IF) and imaging interpretation (II). True (occult) incident breast cancers were also documented. Percentage estimations for these causes were calculated. MAIN OUTCOME MEASURES Breast cancer detection on follow-up screening. SAMPLE SIZE 943 women. RESULTS Of 15 (1.6%) screening-detected breast cancers, 7 cases (46.6%) were missed on the initial screen; 3 (43%) of these were II related, 2 (28.5%) of each were IA and IF. The remaining true (occult) cases were detected on either the second (5 cases) or third screens (3 cases). CONCLUSION Improved screening facilities, quality mammographic acquisition and interpretation, double reading, and implementation of an organized screening program may help to avoid missed breast cancers. LIMITATIONS Retrospective, small sample, single center, and short duration study. CONFLICT OF INTEREST None.
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Affiliation(s)
- Khawaja Bilal Waheed
- From the Department of Radiology, King Fahd Military Medical Complex, Dhahran, Saudi Arabia
| | - Muhammad Zia Ul Hassan
- From the Department of Radiology, King Fahd Military Medical Complex, Dhahran, Saudi Arabia
| | - Donya Al Hassan
- From the Department of Radiology, King Fahd Military Medical Complex, Dhahran, Saudi Arabia
| | | | - Muneera Al Bassam
- From the Department of Radiology, King Fahd Military Medical Complex, Dhahran, Saudi Arabia
| | - Ahmed Aly Elbyali
- From the Department of Radiology, King Fahd Military Medical Complex, Dhahran, Saudi Arabia
| | - Tamer Mohamed Shams
- From the Department of Radiology, King Fahd Military Medical Complex, Dhahran, Saudi Arabia
| | - Zainab Ahmed Demiati
- From the Department of Radiology, King Fahd Military Medical Complex, Dhahran, Saudi Arabia
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Zou L, Yu S, Meng T, Zhang Z, Liang X, Xie Y. A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2019; 2019:6509357. [PMID: 31019547 PMCID: PMC6452645 DOI: 10.1155/2019/6509357] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 02/25/2019] [Indexed: 12/27/2022]
Abstract
This study reviews the technique of convolutional neural network (CNN) applied in a specific field of mammographic breast cancer diagnosis (MBCD). It aims to provide several clues on how to use CNN for related tasks. MBCD is a long-standing problem, and massive computer-aided diagnosis models have been proposed. The models of CNN-based MBCD can be broadly categorized into three groups. One is to design shallow or to modify existing models to decrease the time cost as well as the number of instances for training; another is to make the best use of a pretrained CNN by transfer learning and fine-tuning; the third is to take advantage of CNN models for feature extraction, and the differentiation of malignant lesions from benign ones is fulfilled by using machine learning classifiers. This study enrolls peer-reviewed journal publications and presents technical details and pros and cons of each model. Furthermore, the findings, challenges and limitations are summarized and some clues on the future work are also given. Conclusively, CNN-based MBCD is at its early stage, and there is still a long way ahead in achieving the ultimate goal of using deep learning tools to facilitate clinical practice. This review benefits scientific researchers, industrial engineers, and those who are devoted to intelligent cancer diagnosis.
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Affiliation(s)
- Lian Zou
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China
- Cancer Center of Sichuan Provincial People's Hospital, Chengdu, China
| | - Shaode Yu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Tiebao Meng
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Zhicheng Zhang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China
| | - Xiaokun Liang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China
- Medical Physics Division in the Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA
| | - Yaoqin Xie
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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Breast Cancer Detection in Thermal Infrared Images Using Representation Learning and Texture Analysis Methods. ELECTRONICS 2019. [DOI: 10.3390/electronics8010100] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Nowadays, breast cancer is one of the most common cancers diagnosed in women. Mammography is the standard screening imaging technique for the early detection of breast cancer. However, thermal infrared images (thermographies) can be used to reveal lesions in dense breasts. In these images, the temperature of the regions that contain tumors is warmer than the normal tissue. To detect that difference in temperature between normal and cancerous regions, a dynamic thermography procedure uses thermal infrared cameras to generate infrared images at fixed time steps, obtaining a sequence of infrared images. In this paper, we propose a novel method to model the changes on temperatures in normal and abnormal breasts using a representation learning technique called learning-to-rank and texture analysis methods. The proposed method generates a compact representation for the infrared images of each sequence, which is then exploited to differentiate between normal and cancerous cases. Our method produced competitive (AUC = 0.989) results when compared to other studies in the literature.
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