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For: Mambou SJ, Maresova P, Krejcar O, Selamat A, Kuca K. Breast Cancer Detection Using Infrared Thermal Imaging and a Deep Learning Model. Sensors (Basel) 2018;18:E2799. [PMID: 30149621 DOI: 10.3390/s18092799] [Cited by in Crossref: 66] [Cited by in F6Publishing: 52] [Article Influence: 16.5] [Reference Citation Analysis]
Number Citing Articles
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12 Lema DG, Pedrayes OD, Usamentiaga R, Venegas P, Garcia DF. Automated Detection of Subsurface Defects Using Active Thermography and Deep Learning Object Detectors. IEEE Trans Instrum Meas 2022;71:1-13. [DOI: 10.1109/tim.2022.3169484] [Reference Citation Analysis]
13 Mridha MF, Hamid MA, Monowar MM, Keya AJ, Ohi AQ, Islam MR, Kim JM. A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis. Cancers (Basel) 2021;13:6116. [PMID: 34885225 DOI: 10.3390/cancers13236116] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
14 Ali S, Li J, Pei Y, Khurram R, Rehman KU, Rasool AB. State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods. Cancers (Basel) 2021;13:5546. [PMID: 34771708 DOI: 10.3390/cancers13215546] [Reference Citation Analysis]
15 Diniz de Lima E, Souza Paulino JA, Lira de Farias Freitas AP, Viana Ferreira JE, Barbosa JDS, Bezerra Silva DF, Bento PM, Araújo Maia Amorim AM, Melo DP. Artificial intelligence and infrared thermography as auxiliary tools in the diagnosis of temporomandibular disorder. Dentomaxillofac Radiol 2021;:20210318. [PMID: 34613829 DOI: 10.1259/dmfr.20210318] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
16 Tashakori M, Nahvi A, Ebrahimian Hadi Kiashari S. Driver drowsiness detection using facial thermal imaging in a driving simulator. Proc Inst Mech Eng H 2022;236:43-55. [PMID: 34477030 DOI: 10.1177/09544119211044232] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
17 Gupta KK, Rituvijay, Pahadiya P, Saxena S. Detection of cancer in breast thermograms using mathematical threshold based segmentation and morphology technique. Int J Syst Assur Eng Manag 2022;13:421-8. [DOI: 10.1007/s13198-021-01289-3] [Reference Citation Analysis]
18 P. D, A. B, Ponniah T. Machine Learning Model for Breast Cancer Data Analysis Using Triplet Feature Selection Algorithm. IETE Journal of Research. [DOI: 10.1080/03772063.2021.1963861] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
19 Hakim A, Awale RN. Designing a Three-layer Back Propagation Artificial Neural Network for Breast Thermogram Classification. IETE Journal of Research. [DOI: 10.1080/03772063.2021.1958074] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
20 Al Husaini MAS, Habaebi MH, Gunawan TS, Islam MR, Elsheikh EAA, Suliman FM. Thermal-based early breast cancer detection using inception V3, inception V4 and modified inception MV4. Neural Comput Appl 2021;:1-16. [PMID: 34393379 DOI: 10.1007/s00521-021-06372-1] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
21 Chang HW, Frey G, Liu H, Xing C, Steinman L, Boyle WJ, Short JM. Generating tumor-selective conditionally active biologic anti-CTLA4 antibodies via protein-associated chemical switches. Proc Natl Acad Sci U S A 2021;118:e2020606118. [PMID: 33627407 DOI: 10.1073/pnas.2020606118] [Cited by in Crossref: 1] [Cited by in F6Publishing: 9] [Article Influence: 1.0] [Reference Citation Analysis]
22 Lin X, Li Z, Qiu J, Wang Q, Wang J, Zhang H, Chen T. Fascinating MXene nanomaterials: emerging opportunities in the biomedical field. Biomater Sci 2021;9:5437-71. [PMID: 34296233 DOI: 10.1039/d1bm00526j] [Cited by in F6Publishing: 12] [Reference Citation Analysis]
23 Thapa A, Alsadoon A, Prasad PWC, Bajaj S, Alsadoon OH, Rashid TA, Ali RS, Jerew OD. Deep learning for breast cancer classification: Enhanced tangent function. Computational Intelligence. [DOI: 10.1111/coin.12476] [Reference Citation Analysis]
24 Leite ML, de Loiola Costa LS, Cunha VA, Kreniski V, de Oliveira Braga Filho M, da Cunha NB, Costa FF. Artificial intelligence and the future of life sciences. Drug Discov Today 2021:S1359-6446(21)00308-1. [PMID: 34245910 DOI: 10.1016/j.drudis.2021.07.002] [Reference Citation Analysis]
25 Javan AAK, Jafari M, Shoeibi A, Zare A, Khodatars M, Ghassemi N, Alizadehsani R, Gorriz JM. Medical Images Encryption Based on Adaptive-Robust Multi-Mode Synchronization of Chen Hyper-Chaotic Systems. Sensors (Basel) 2021;21:3925. [PMID: 34200287 DOI: 10.3390/s21113925] [Cited by in Crossref: 1] [Cited by in F6Publishing: 6] [Article Influence: 1.0] [Reference Citation Analysis]
26 Al Masry Z, Zerhouni N, Gay C, Meraghni S, Lodi M, Mathelin C, Devalland C. [Connected bras for breast cancer detection in 2021: Analysis and perspectives]. Gynecol Obstet Fertil Senol 2021:S2468-7189(21)00156-2. [PMID: 34091080 DOI: 10.1016/j.gofs.2021.05.008] [Reference Citation Analysis]
27 Liu W, Long M, Peng L, Qu C, Guo R, Kang Z, Wang J, Wu J, Wang X. Digital breast tomosynthesis improves diagnostic accuracy of breast microcalcifications. Int J Imaging Syst Technol 2021;31:555-61. [DOI: 10.1002/ima.22481] [Reference Citation Analysis]
28 Cherian Kurian N, Sethi A, Reddy Konduru A, Mahajan A, Rane SU. A 2021 update on cancer image analytics with deep learning. WIREs Data Mining Knowl Discov 2021;11. [DOI: 10.1002/widm.1410] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
29 Tariq M, Iqbal S, Ayesha H, Abbas I, Ahmad KT, Niazi MFK. Medical image based breast cancer diagnosis: State of the art and future directions. Expert Systems with Applications 2021;167:114095. [DOI: 10.1016/j.eswa.2020.114095] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 8.0] [Reference Citation Analysis]
30 Tarakanov AV, Tarakanov AA, Vesnin S, Efremov VV, Goryanin I, Roberts N. Microwave Radiometry (MWR) temperature measurement is related to symptom severity in patients with Low Back Pain (LBP). J Bodyw Mov Ther 2021;26:548-52. [PMID: 33992296 DOI: 10.1016/j.jbmt.2021.02.005] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
31 Yousefi B, Akbari H, Maldague XPV. Detecting Vasodilation as Potential Diagnostic Biomarker in Breast Cancer Using Deep Learning-Driven Thermomics. Biosensors (Basel) 2020;10:E164. [PMID: 33142939 DOI: 10.3390/bios10110164] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 2.5] [Reference Citation Analysis]
32 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 2021;9:131-45. [DOI: 10.1080/21681163.2020.1824685] [Cited by in Crossref: 24] [Cited by in F6Publishing: 8] [Article Influence: 12.0] [Reference Citation Analysis]
33 Manda MP, Kim HS. A Fast Image Thresholding Algorithm for Infrared Images Based on Histogram Approximation and Circuit Theory. Algorithms 2020;13:207. [DOI: 10.3390/a13090207] [Cited by in Crossref: 6] [Cited by in F6Publishing: 11] [Article Influence: 3.0] [Reference Citation Analysis]
34 Mewada HK, Patel AV, Hassaballah M, Alkinani MH, Mahant K. Spectral-Spatial Features Integrated Convolution Neural Network for Breast Cancer Classification. Sensors (Basel) 2020;20:E4747. [PMID: 32842640 DOI: 10.3390/s20174747] [Cited by in Crossref: 2] [Cited by in F6Publishing: 14] [Article Influence: 1.0] [Reference Citation Analysis]
35 Taheri-garavand A, Nasiri A, Banan A, Zhang Y. Smart deep learning-based approach for non-destructive freshness diagnosis of common carp fish. Journal of Food Engineering 2020;278:109930. [DOI: 10.1016/j.jfoodeng.2020.109930] [Cited by in Crossref: 17] [Cited by in F6Publishing: 27] [Article Influence: 8.5] [Reference Citation Analysis]
36 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] [Cited by in Crossref: 6] [Cited by in F6Publishing: 9] [Article Influence: 3.0] [Reference Citation Analysis]
37 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 DOI: 10.1097/MD.0000000000020251] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
38 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;9:245-59. [PMID: 35638673 DOI: 10.1021/acsmacrolett.9b00948] [Cited by in Crossref: 34] [Cited by in F6Publishing: 33] [Article Influence: 17.0] [Reference Citation Analysis]
39 Acharya S, Alsadoon A, Prasad PWC, Abdullah S, Deva A. Deep convolutional network for breast cancer classification: enhanced loss function (ELF). J Supercomput 2020;76:8548-65. [DOI: 10.1007/s11227-020-03157-6] [Cited by in Crossref: 10] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
40 Rusli N, Sidek SN, Yusof HM, Ishak NI, Khalid M, Dzulkarnain AAA. Implementation of Wavelet Analysis on Thermal Images for Affective States Recognition of Children With Autism Spectrum Disorder. IEEE Access 2020;8:120818-34. [DOI: 10.1109/access.2020.3006004] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
41 Nasiri A, Taheri-garavand A, Omid M, Carlomagno GM. Intelligent fault diagnosis of cooling radiator based on deep learning analysis of infrared thermal images. Applied Thermal Engineering 2019;163:114410. [DOI: 10.1016/j.applthermaleng.2019.114410] [Cited by in Crossref: 24] [Cited by in F6Publishing: 15] [Article Influence: 8.0] [Reference Citation Analysis]
42 Pauk J, Ihnatouski M, Wasilewska A. Detection of inflammation from finger temperature profile in rheumatoid arthritis. Med Biol Eng Comput 2019;57:2629-39. [PMID: 31679125 DOI: 10.1007/s11517-019-02055-1] [Cited by in Crossref: 2] [Cited by in F6Publishing: 6] [Article Influence: 0.7] [Reference Citation Analysis]
43 Raghavendra U, Gudigar A, Rao TN, Ciaccio EJ, Ng E, Rajendra Acharya U. Computer-aided diagnosis for the identification of breast cancer using thermogram images: A comprehensive review. Infrared Physics & Technology 2019;102:103041. [DOI: 10.1016/j.infrared.2019.103041] [Cited by in Crossref: 19] [Cited by in F6Publishing: 10] [Article Influence: 6.3] [Reference Citation Analysis]
44 González-patiño, Villuendas-rey, Argüelles-cruz, Karray. A Novel Bio-Inspired Method for Early Diagnosis of Breast Cancer through Mammographic Image Analysis. Applied Sciences 2019;9:4492. [DOI: 10.3390/app9214492] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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46 Zuluaga-gomez J, Zerhouni N, Al Masry Z, Devalland C, Varnier C. A survey of breast cancer screening techniques: thermography and electrical impedance tomography. Journal of Medical Engineering & Technology 2019;43:305-22. [DOI: 10.1080/03091902.2019.1664672] [Cited by in Crossref: 9] [Cited by in F6Publishing: 14] [Article Influence: 3.0] [Reference Citation Analysis]
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48 Mambou, Krejcar, Maresova, Selamat, Kuca. Novel Hand Gesture Alert System. Applied Sciences 2019;9:3419. [DOI: 10.3390/app9163419] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 2.3] [Reference Citation Analysis]
49 Pauk J, Wasilewska A, Ihnatouski M. Infrared Thermography Sensor for Disease Activity Detection in Rheumatoid Arthritis Patients. Sensors (Basel) 2019;19:E3444. [PMID: 31394720 DOI: 10.3390/s19163444] [Cited by in Crossref: 14] [Cited by in F6Publishing: 20] [Article Influence: 4.7] [Reference Citation Analysis]
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51 Zou L, Yu S, Meng T, Zhang Z, Liang X, Xie Y. A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis. Comput Math Methods Med 2019;2019:6509357. [PMID: 31019547 DOI: 10.1155/2019/6509357] [Cited by in Crossref: 28] [Cited by in F6Publishing: 30] [Article Influence: 9.3] [Reference Citation Analysis]
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