A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis
Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effecti...
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| Published in | Archives of computational methods in engineering Vol. 29; no. 4; pp. 2043 - 2070 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.06.2022
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required. |
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| AbstractList | Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required.Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required. Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required. |
| Author | Gupta, Surbhi Singla, Ruchi Hu, Yu-Chen Kumar, Yogesh |
| Author_xml | – sequence: 1 givenname: Yogesh orcidid: 0000-0002-2879-0441 surname: Kumar fullname: Kumar, Yogesh email: yogesh.arora10744@gmail.com organization: Department of Computer Engineering, Indus Institute of Technology & Engineering, Indus University – sequence: 2 givenname: Surbhi surname: Gupta fullname: Gupta, Surbhi organization: School of Computer Science and Engineering, Model Institute of Engineering and Technology – sequence: 3 givenname: Ruchi surname: Singla fullname: Singla, Ruchi organization: Department of Research, Innovations, Sponsored Projects and Entrepreneurship, Chandigarh Group of Colleges – sequence: 4 givenname: Yu-Chen surname: Hu fullname: Hu, Yu-Chen organization: Department of Computer Science and Information Management, Providence University |
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| Cites_doi | 10.21037/atm.2019.04.34 10.3390/cancers11091235 10.1186/s12880-021-00599-z 10.3390/ai1010003 10.1109/ACCESS.2019.2929365 10.1042/BSR20181769 10.1007/978-3-319-60964-5_44 10.1162/NECO_a_00312 10.1007/s00432-019-03117-5 10.1038/s41598-019-55972-4 10.1109/CoCoNet.2015.7411260 10.1155/2019/6212759 10.18280/ts.360406 10.1016/j.patcog.2009.05.012 10.3390/electronics9030445 10.1109/IC4ME247184.2019.9036653 10.1007/978-3-030-70604-3_6 10.1109/APCASE.2015.12 10.1111/coin.12452 10.1056/NEJMoa1008069 10.1039/c3an36890d 10.21786/bbrc/13.11/13 10.4251/wjgo.v11.i12.1218 10.3390/designs2020013 10.1002/ijc.25516 10.1007/s10120-018-0793-2 10.1093/comjnl/bxaa198 10.1109/ACCESS.2020.3004056 10.1007/s10916-019-1397-z 10.1371/journal.pone.0210551 10.1109/ICET.2016.7813261 10.3390/jimaging6060039 10.1016/j.protcy.2016.05.126 10.35940/ijeat.a1116.1291s419 10.1109/EMBC.2018.8513274 10.1155/2018/6456724 10.1111/j.1365-4632.2010.04474.x 10.1109/ATEE.2019.8724938 10.1016/j.fcij.2017.12.001 10.1007/s00432-020-03249-z 10.1155/2019/1074286 10.1186/1758-3284-3-33 10.1016/j.procs.2017.01.201 10.1016/j.canep.2011.05.002 10.1007/s00432-020-03305-8 10.1007/s10462-019-09716-5 10.1016/j.protcy.2016.05.153 10.1007/978-3-642-40763-5_78 10.1016/j.sder.2011.01.002 10.3390/app10113842 10.1016/j.cogsys.2018.12.009 10.1038/s41598-019-50567-5 10.1001/jama.2012.5521 10.1109/JBHI.2016.2635662 10.1007/s00432-020-03338-z 10.1007/978-3-030-31672-3_1 10.1109/ACCESS.2020.3008868 10.1515/med-2020-0006 10.1007/s12065-020-00403-x 10.1186/s13045-019-0833-3 10.1136/bmjgast-2019-000371 10.1109/ISSPIT47144.2019.9001790 10.1007/s11831-021-09554-1 10.1145/3330482.3330525 10.1007/s11831-020-09470-w 10.31557/APJCP.2019.20.7.2095 10.1016/j.procs.2016.06.105 10.1056/NEJMoa0810696 10.1016/j.clinthera.2016.03.026 10.4066/biomedicalresearch.29-18-1106 10.1007/s00432-020-03206-w 10.1007/s10278-019-00230-2 10.1016/j.imu.2019.01.001 10.1155/2019/8321942 10.13053/CyS-22-3-2526 10.1155/2020/1963814 10.1002/ima.22123 10.24926/548719.023 10.1155/2018/8087624 10.1007/s10462-019-09735-2 10.1016/j.eswa.2015.02.005 10.1007/s00439-009-0722-x 10.1016/j.eswa.2019.112951 10.1145/3338472.3338492 10.1145/2222444.2222467 10.1155/2019/4909846 10.1109/ACCESS.2019.2936017 10.3390/s19173722 10.3348/kjr.2019.0752 10.1038/s41598-018-33860-7 10.1109/ACCESS.2018.2856402 10.1109/ACCESS.2019.2906241 10.1049/trit.2019.0077 10.1038/s41598-016-0028-x 10.1016/j.oraloncology.2019.03.011 10.1007/s00432-015-2086-z 10.3390/app10051800 10.1109/EMBC.2013.6610529 10.1007/s10462-020-09865-y 10.19044/esj.2017.v13n3p342 10.3390/bdcc3020027 10.1117/12.2277121 10.1007/s00432-019-03098-5 10.1186/s12880-019-0307-7 10.1109/ICECDS.2017.8389744 10.1007/978-81-322-2217-0_23 10.1056/NEJMoa1014618 10.1038/s41598-019-46718-3 10.1109/CSCS.2017.56 10.3390/app8122422 10.1007/s00432-020-03232-8 10.1109/ACCESS.2019.2904245 10.1371/journal.pone.0195875 10.7717/peerj.6201 10.1109/STSIVA.2019.8730284 10.1097/MOP.0b013e328343f4dd 10.1109/ACCESS.2019.2926837 10.1101/324541 10.1136/bmj.f4706 10.1007/s00432-020-03291-x 10.1155/2014/842037 10.1007/s11938-020-00274-2 10.17148/IJREEICE.2015.3229 10.1186/s12880-021-00608-1 10.3390/a8041088 10.1109/BMEiCON.2018.8609997 10.1109/SampTA45681.2019.9030909 10.1155/2016/3219068 10.1007/s00432-020-03172-3 10.3390/app10207292 10.1038/s41598-020-57670-y 10.1186/s12880-021-00563-x 10.5935/1676-2444.20200013 10.1109/IST.2016.7738230 10.1111/j.1399-302X.2008.00479.x 10.1007/978-981-10-6890-4_11 10.1186/s12880-021-00594-4 10.1109/ICCUBEA.2018.8697804 10.1186/s12880-021-00566-8 10.1007/s00432-016-2131-6 10.3389/fdata.2020.00006 10.1007/s00432-020-03318-3 10.3390/app10093134 10.1093/annonc/mdp391 10.1155/2011/182435 10.1007/s00521-020-04842-6 10.1155/2020/9162464 10.1155/2013/942353 10.1371/journal.pone.0217293 10.1007/s00432-020-03304-9 10.1007/s00432-020-03264-0 10.5829/idosi.mejsr.2017.417.426 10.2147/BCTT.S176070 10.1007/s00432-018-2595-7 10.1186/1471-2288-9-74 10.1007/s11042-019-08394-3 10.1186/s12859-017-1922-3 |
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| References | 9648_CR52 9648_CR50 M Donaldson (9648_CR48) 2011; 30 9648_CR51 M Wong (9648_CR174) 2017; 7 SA Azer (9648_CR24) 2019; 11 M Albahar (9648_CR8) 2019; 7 N Hoerter (9648_CR73) 2020; 18 9648_CR100 9648_CR101 A Lathwal (9648_CR95) 2020 A Saha (9648_CR144) 2018; 144 MA Devi (9648_CR44) 2016; 89 T Kalaiselvi (9648_CR80) 2015; 25 M Nishio (9648_CR127) 2018; 13 J Engel (9648_CR53) 2020; 146 H Mohsen (9648_CR112) 2018; 3 AKHS Kurnianingsih (9648_CR92) 2019; 7 9648_CR58 9648_CR108 9648_CR59 H Lee (9648_CR103) 2015; 42 L Lopez (9648_CR104) 2020 M Sokoutil (9648_CR161) 2018; 18 MY Abdallah (9648_CR2) 2018; 29 TA Sajenna (9648_CR143) 2015; 2015 AM Weng (9648_CR170) 2021; 21 C Kaushal (9648_CR83) 2020 Y Zhang (9648_CR184) 2019; 12 S Sasikala (9648_CR150) 2018; 8 9648_CR45 K Hosny (9648_CR76) 2019 9648_CR46 9648_CR43 S Han (9648_CR67) 2019; 32 A Milne (9648_CR116) 2009; 126 HJ Park (9648_CR130) 2020; 21 R Gupta (9648_CR62) 2017; 4 N Hu (9648_CR74) 2021; 21 J Ferlay (9648_CR55) 2010; 127 9648_CR49 A Sarwar (9648_CR149) 2020; 53 P Jin (9648_CR77) 2020; 146 9648_CR115 BJ Nartowt (9648_CR121) 2020; 3 9648_CR34 TM Mejia (9648_CR111) 2015; 2015 9648_CR35 S Feng (9648_CR54) 2013; 138 9648_CR32 R Mehrotra (9648_CR110) 2011; 3 Q Nguyen (9648_CR124) 2015; 1 YR Chillakuru (9648_CR39) 2021; 21 M Nivaashini (9648_CR128) 2018; 119 G Andriole (9648_CR10) 2009; 360 V Kadam (9648_CR79) 2019; 43 9648_CR167 L Wang (9648_CR168) 2012; 36 N Zhang (9648_CR182) 2016; 142 W Yue (9648_CR179) 2018; 2 M Yamada (9648_CR175) 2019; 9 K Nakano (9648_CR119) 2009; 24 D Selvathi (9648_CR152) 2017; 25 RS Patil (9648_CR131) 2020 T Hirasawa (9648_CR72) 2018; 21 J Bono (9648_CR30) 2011; 364 G Murtaza (9648_CR114) 2020; 53 W Chen (9648_CR37) 2020; 10 F Bidard (9648_CR29) 2010; 24 9648_CR23 9648_CR21 J Saraf (9648_CR148) 2017; 4 9648_CR180 A Assiri (9648_CR14) 2020; 6 Y Kumar (9648_CR91) 2019; 8 WGE Gonçalves (9648_CR57) 2020; 7 S Naeem (9648_CR117) 2020; 10 L Zhang (9648_CR181) 2020; 15 K Sudharani (9648_CR163) 2016; 24 9648_CR176 F Haggar (9648_CR66) 2009; 22 9648_CR4 9648_CR5 9648_CR27 9648_CR6 9648_CR28 A Suberi (9648_CR162) 2016; 105 M Malathi (9648_CR107) 2019; 20 TN Le (9648_CR96) 2016; 2016 9648_CR97 9648_CR94 9648_CR12 9648_CR98 9648_CR11 9648_CR99 X Wei (9648_CR169) 2020; 146 N Azamjah (9648_CR13) 2018; 20 G Cui (9648_CR41) 2019; 14 R Rosalidar (9648_CR140) 2020; 8 MQ Khan (9648_CR84) 2019; 7 9648_CR90 N Yu (9648_CR178) 2017; 18 9648_CR146 PN Figueiredo (9648_CR56) 2011 9648_CR147 9648_CR145 9648_CR142 A Asuntha (9648_CR22) 2020; 79 A Marka (9648_CR109) 2019 9648_CR141 L Ma (9648_CR106) 2019 S Ramadan (9648_CR136) 2020; 2020 9648_CR16 D Beilner (9648_CR26) 2020 A Raj (9648_CR137) 2016; 24 9648_CR18 9648_CR19 L Alzubaidi (9648_CR9) 2020; 9 9648_CR85 9648_CR87 9648_CR88 9648_CR160 AM Bur (9648_CR33) 2019; 92 9648_CR81 S Yoo (9648_CR177) 2019; 9 9648_CR155 Y Ning (9648_CR125) 2017; 18 9648_CR151 C Donald (9648_CR47) 2011; 364 CWH Chan (9648_CR36) 2020; 146 KP Win (9648_CR171) 2020; 10 H Cheng (9648_CR38) 2010; 43 9648_CR75 D Narayanan (9648_CR120) 2010; 49 9648_CR78 J Kloeckner (9648_CR86) 2020; 56 S Tabibu (9648_CR164) 2019; 9 T Song (9648_CR157) 2021; 21 DT Kruger (9648_CR89) 2020 AI Iuga (9648_CR105) 2021; 21 A Abraham (9648_CR3) 2009; 9 L Lavanya (9648_CR93) 2019; 12 P Bach (9648_CR25) 2012; 307 R Zhang (9648_CR183) 2016; 21 K Hertrampf (9648_CR71) 2020; 146 KK Senthil (9648_CR153) 2019; 2019 M Wu (9648_CR173) 2018; 38 F Altaf (9648_CR1) 2019; 7 F Taher (9648_CR165) 2015; 8 J Liu (9648_CR102) 2020; 146 SH Nallamala (9648_CR123) 2019; 8 P Poudel (9648_CR132) 2018 9648_CR129 9648_CR126 9648_CR63 K Munir (9648_CR113) 2019; 11 N Nasrullah (9648_CR122) 2019; 19 9648_CR64 9648_CR61 Y Shin (9648_CR159) 2018; 6 EA Boakye (9648_CR15) 2020; 146 9648_CR68 P Kaur (9648_CR82) 2019; 16 9648_CR65 A Das (9648_CR42) 2019; 54 Z Alyafeai (9648_CR20) 2020 J Büntzel (9648_CR31) 2020 Z Sobhaninia (9648_CR156) 2020; 2 9648_CR60 PM Shakeel (9648_CR154) 2020 VK Nahar (9648_CR118) 2016; 142 9648_CR133 DA Ragab (9648_CR135) 2019; 1 9648_CR134 S Alam (9648_CR17) 2019; 3 S Thapa (9648_CR166) 2019 T Shibata (9648_CR158) 2020; 10 G Chlebus (9648_CR40) 2018; 8 9648_CR69 J Zugazagoitia (9648_CR185) 2016; 38 9648_CR139 W Alakwaa (9648_CR7) 2017; 8 Z He (9648_CR70) 2020; 10 Y Win (9648_CR172) 2018; 2018 9648_CR138 |
| References_xml | – volume: 1 start-page: 1 year: 2015 ident: 9648_CR124 publication-title: Clinical – ident: 9648_CR61 doi: 10.21037/atm.2019.04.34 – volume: 11 start-page: 1 year: 2019 ident: 9648_CR113 publication-title: Cancers doi: 10.3390/cancers11091235 – volume: 21 start-page: 1 issue: 1 year: 2021 ident: 9648_CR105 publication-title: BMC Med Imaging doi: 10.1186/s12880-021-00599-z – volume: 18 start-page: 1 issue: 4 year: 2018 ident: 9648_CR161 publication-title: Int J Comput Sci Network Secur – ident: 9648_CR139 doi: 10.3390/ai1010003 – volume: 7 start-page: 1 year: 2019 ident: 9648_CR1 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2929365 – volume: 38 start-page: 1 issue: 6 year: 2018 ident: 9648_CR173 publication-title: Biosci Rep doi: 10.1042/BSR20181769 – ident: 9648_CR50 doi: 10.1007/978-3-319-60964-5_44 – ident: 9648_CR43 doi: 10.1162/NECO_a_00312 – volume: 4 start-page: 1 issue: 5 year: 2017 ident: 9648_CR62 publication-title: Int J Contemp Med Res – volume: 146 start-page: 297 issue: 2 year: 2020 ident: 9648_CR169 publication-title: J Cancer Res Clin Oncol doi: 10.1007/s00432-019-03117-5 – ident: 9648_CR32 – volume: 18 start-page: 1 year: 2017 ident: 9648_CR125 publication-title: BMC Bioinformatics – volume: 9 start-page: 19518 year: 2019 ident: 9648_CR177 publication-title: Sci Rep doi: 10.1038/s41598-019-55972-4 – ident: 9648_CR16 doi: 10.1109/CoCoNet.2015.7411260 – year: 2019 ident: 9648_CR106 publication-title: Comput Intell Neurosci doi: 10.1155/2019/6212759 – ident: 9648_CR145 doi: 10.18280/ts.360406 – volume: 43 start-page: 299 issue: 1 year: 2010 ident: 9648_CR38 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2009.05.012 – volume: 9 start-page: 1 year: 2020 ident: 9648_CR9 publication-title: Electronics doi: 10.3390/electronics9030445 – ident: 9648_CR69 doi: 10.1109/IC4ME247184.2019.9036653 – ident: 9648_CR27 doi: 10.1007/978-3-030-70604-3_6 – volume: 2015 start-page: 24 year: 2015 ident: 9648_CR111 publication-title: Asia-Pacific Conf Comput-Aid Syst Eng doi: 10.1109/APCASE.2015.12 – ident: 9648_CR63 doi: 10.1111/coin.12452 – volume: 8 start-page: 259 year: 2018 ident: 9648_CR150 publication-title: Int J Innov Technol Explor Eng – volume: 364 start-page: 2091 year: 2011 ident: 9648_CR47 publication-title: N Engl J Med doi: 10.1056/NEJMoa1008069 – volume: 138 start-page: 3967 year: 2013 ident: 9648_CR54 publication-title: Analyst doi: 10.1039/c3an36890d – ident: 9648_CR151 doi: 10.21786/bbrc/13.11/13 – volume: 11 start-page: 1218 issue: 12 year: 2019 ident: 9648_CR24 publication-title: World J Gastrointest Oncol doi: 10.4251/wjgo.v11.i12.1218 – volume: 2 start-page: 1 issue: 13 year: 2018 ident: 9648_CR179 publication-title: Designs doi: 10.3390/designs2020013 – volume: 127 start-page: 2893 year: 2010 ident: 9648_CR55 publication-title: Int J Cancer doi: 10.1002/ijc.25516 – volume: 21 start-page: 653 issue: 4 year: 2018 ident: 9648_CR72 publication-title: Gastric Cancer doi: 10.1007/s10120-018-0793-2 – ident: 9648_CR64 doi: 10.1093/comjnl/bxaa198 – volume: 8 start-page: 116176 year: 2020 ident: 9648_CR140 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3004056 – volume: 43 start-page: 1 year: 2019 ident: 9648_CR79 publication-title: Image Signal Process doi: 10.1007/s10916-019-1397-z – volume: 14 start-page: 1 issue: 1 year: 2019 ident: 9648_CR41 publication-title: PLoS ONE doi: 10.1371/journal.pone.0210551 – ident: 9648_CR133 doi: 10.1109/ICET.2016.7813261 – volume: 6 start-page: 1 issue: 39 year: 2020 ident: 9648_CR14 publication-title: J Imaging doi: 10.3390/jimaging6060039 – volume: 24 start-page: 1305 year: 2016 ident: 9648_CR137 publication-title: Procedia Technol doi: 10.1016/j.protcy.2016.05.126 – ident: 9648_CR52 – ident: 9648_CR138 doi: 10.35940/ijeat.a1116.1291s419 – ident: 9648_CR146 doi: 10.1109/EMBC.2018.8513274 – volume: 2018 start-page: 1 year: 2018 ident: 9648_CR172 publication-title: Hindawi doi: 10.1155/2018/6456724 – volume: 49 start-page: 978 year: 2010 ident: 9648_CR120 publication-title: Int J Dermatol doi: 10.1111/j.1365-4632.2010.04474.x – ident: 9648_CR134 doi: 10.1109/ATEE.2019.8724938 – volume: 3 start-page: 68 issue: 1 year: 2018 ident: 9648_CR112 publication-title: Future Comput Inform J doi: 10.1016/j.fcij.2017.12.001 – year: 2020 ident: 9648_CR31 publication-title: J Cancer Res Clin Oncol doi: 10.1007/s00432-020-03249-z – ident: 9648_CR142 doi: 10.1155/2019/1074286 – volume: 3 start-page: 1 year: 2011 ident: 9648_CR110 publication-title: Head Neck Oncol doi: 10.1186/1758-3284-3-33 – volume: 105 start-page: 177 year: 2016 ident: 9648_CR162 publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2017.01.201 – volume: 36 start-page: 61 year: 2012 ident: 9648_CR168 publication-title: Cancer Epidemiol doi: 10.1016/j.canep.2011.05.002 – volume: 8 start-page: 409 issue: 8 year: 2017 ident: 9648_CR7 publication-title: Int J Adv Comput Sci Appl – volume: 146 start-page: 2489 issue: 10 year: 2020 ident: 9648_CR71 publication-title: J Cancer Res Clin Oncol doi: 10.1007/s00432-020-03305-8 – volume: 53 start-page: 1655 issue: 3 year: 2020 ident: 9648_CR114 publication-title: Artif Intell Rev doi: 10.1007/s10462-019-09716-5 – volume: 2015 start-page: 663 year: 2015 ident: 9648_CR143 publication-title: Int Conf Comput Network Commun Trivandrum – volume: 24 start-page: 1374 year: 2016 ident: 9648_CR163 publication-title: Procedia Technol doi: 10.1016/j.protcy.2016.05.153 – ident: 9648_CR35 doi: 10.1007/978-3-642-40763-5_78 – volume: 30 start-page: 3 year: 2011 ident: 9648_CR48 publication-title: Semin Cutan Med Surg doi: 10.1016/j.sder.2011.01.002 – volume: 10 start-page: 1 issue: 11 year: 2020 ident: 9648_CR158 publication-title: Appl Sci (Switzerland) doi: 10.3390/app10113842 – volume: 54 start-page: 165 year: 2019 ident: 9648_CR42 publication-title: Cogn Syst Res doi: 10.1016/j.cogsys.2018.12.009 – volume: 9 start-page: 14465 year: 2019 ident: 9648_CR175 publication-title: Sci Rep doi: 10.1038/s41598-019-50567-5 – volume: 307 start-page: 2418 year: 2012 ident: 9648_CR25 publication-title: JAMA doi: 10.1001/jama.2012.5521 – volume: 21 start-page: 41 issue: 1 year: 2016 ident: 9648_CR183 publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2016.2635662 – year: 2020 ident: 9648_CR26 publication-title: J Cancer Res Clin Oncol doi: 10.1007/s00432-020-03338-z – ident: 9648_CR90 doi: 10.1007/978-3-030-31672-3_1 – year: 2020 ident: 9648_CR104 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3008868 – volume: 15 start-page: 27 issue: 1 year: 2020 ident: 9648_CR181 publication-title: Open Med (Poland) doi: 10.1515/med-2020-0006 – year: 2020 ident: 9648_CR131 publication-title: Evol Intel doi: 10.1007/s12065-020-00403-x – volume: 12 start-page: 1 year: 2019 ident: 9648_CR184 publication-title: J Hematol Oncol doi: 10.1186/s13045-019-0833-3 – volume: 7 start-page: 1 issue: 1 year: 2020 ident: 9648_CR57 publication-title: BMJ Open Gastroenterol doi: 10.1136/bmjgast-2019-000371 – ident: 9648_CR78 doi: 10.1109/ISSPIT47144.2019.9001790 – ident: 9648_CR11 doi: 10.1007/s11831-021-09554-1 – ident: 9648_CR68 doi: 10.1145/3330482.3330525 – ident: 9648_CR88 doi: 10.1007/s11831-020-09470-w – volume: 20 start-page: 2095 issue: 7 year: 2019 ident: 9648_CR107 publication-title: Asian Pac J Cancer Prev doi: 10.31557/APJCP.2019.20.7.2095 – volume: 89 start-page: 465 year: 2016 ident: 9648_CR44 publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2016.06.105 – volume: 360 start-page: 1310 year: 2009 ident: 9648_CR10 publication-title: N Engl J Med doi: 10.1056/NEJMoa0810696 – volume: 38 start-page: 1551 year: 2016 ident: 9648_CR185 publication-title: Clin Ther doi: 10.1016/j.clinthera.2016.03.026 – volume: 29 start-page: 3732 issue: 20 year: 2018 ident: 9648_CR2 publication-title: Biomed Res doi: 10.4066/biomedicalresearch.29-18-1106 – volume: 146 start-page: 1395 issue: 6 year: 2020 ident: 9648_CR36 publication-title: J Cancer Res Clin Oncol doi: 10.1007/s00432-020-03206-w – ident: 9648_CR65 – volume: 32 start-page: 638 issue: 4 year: 2019 ident: 9648_CR67 publication-title: J Digit Imaging doi: 10.1007/s10278-019-00230-2 – volume: 16 year: 2019 ident: 9648_CR82 publication-title: Inform Med Unlock doi: 10.1016/j.imu.2019.01.001 – year: 2019 ident: 9648_CR166 publication-title: Gastroenterol Res Pract doi: 10.1155/2019/8321942 – ident: 9648_CR75 doi: 10.13053/CyS-22-3-2526 – ident: 9648_CR49 doi: 10.1155/2020/1963814 – volume: 25 start-page: 77 issue: 1 year: 2015 ident: 9648_CR80 publication-title: Int J Imaging Syst Technol doi: 10.1002/ima.22123 – ident: 9648_CR147 doi: 10.24926/548719.023 – volume: 12 start-page: 596 issue: 5 year: 2019 ident: 9648_CR93 publication-title: Int J Eng Res Technol – year: 2018 ident: 9648_CR132 publication-title: J Healthcare Eng doi: 10.1155/2018/8087624 – volume: 53 start-page: 2341 issue: 4 year: 2020 ident: 9648_CR149 publication-title: Artif Intell Rev doi: 10.1007/s10462-019-09735-2 – ident: 9648_CR6 – volume: 42 start-page: 5356 year: 2015 ident: 9648_CR103 publication-title: Elsevier doi: 10.1016/j.eswa.2015.02.005 – volume: 126 start-page: 615 year: 2009 ident: 9648_CR116 publication-title: Hum Genet doi: 10.1007/s00439-009-0722-x – year: 2020 ident: 9648_CR20 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2019.112951 – ident: 9648_CR85 doi: 10.1145/3338472.3338492 – ident: 9648_CR18 doi: 10.1145/2222444.2222467 – volume: 2019 start-page: 4909846 year: 2019 ident: 9648_CR153 publication-title: Comput Math Methods Med doi: 10.1155/2019/4909846 – volume: 7 start-page: 116925 year: 2019 ident: 9648_CR92 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2936017 – volume: 19 start-page: 3722 issue: 7 year: 2019 ident: 9648_CR122 publication-title: Sensors doi: 10.3390/s19173722 – volume: 21 start-page: 387 issue: 4 year: 2020 ident: 9648_CR130 publication-title: Korean J Radiol doi: 10.3348/kjr.2019.0752 – volume: 8 start-page: 15497 year: 2018 ident: 9648_CR40 publication-title: Sci Rep doi: 10.1038/s41598-018-33860-7 – volume: 6 start-page: 40950 year: 2018 ident: 9648_CR159 publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2856402 – volume: 7 start-page: 38306 year: 2019 ident: 9648_CR8 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2906241 – year: 2020 ident: 9648_CR83 publication-title: CAAI Trans Intell Technol doi: 10.1049/trit.2019.0077 – volume: 7 start-page: 1 year: 2017 ident: 9648_CR174 publication-title: Sci Rep doi: 10.1038/s41598-016-0028-x – volume: 92 start-page: 20 year: 2019 ident: 9648_CR33 publication-title: Oral Oncol doi: 10.1016/j.oraloncology.2019.03.011 – ident: 9648_CR129 – ident: 9648_CR59 – volume: 142 start-page: 1273 issue: 6 year: 2016 ident: 9648_CR118 publication-title: J Cancer Res Clin Oncol doi: 10.1007/s00432-015-2086-z – volume: 10 start-page: 1 issue: 5 year: 2020 ident: 9648_CR171 publication-title: Appl Sci doi: 10.3390/app10051800 – ident: 9648_CR99 doi: 10.1109/EMBC.2013.6610529 – ident: 9648_CR51 – ident: 9648_CR4 doi: 10.1007/s10462-020-09865-y – ident: 9648_CR5 doi: 10.19044/esj.2017.v13n3p342 – volume: 3 start-page: 1 issue: 2 year: 2019 ident: 9648_CR17 publication-title: Big Data Cogn Comput doi: 10.3390/bdcc3020027 – ident: 9648_CR98 doi: 10.1117/12.2277121 – ident: 9648_CR101 doi: 10.1007/s00432-019-03098-5 – year: 2019 ident: 9648_CR109 publication-title: BMC Med Imaging doi: 10.1186/s12880-019-0307-7 – ident: 9648_CR58 doi: 10.1109/ICECDS.2017.8389744 – ident: 9648_CR141 doi: 10.1007/978-81-322-2217-0_23 – volume: 364 start-page: 1995 year: 2011 ident: 9648_CR30 publication-title: N Engl J Med doi: 10.1056/NEJMoa1014618 – ident: 9648_CR94 – volume: 9 start-page: 10509 year: 2019 ident: 9648_CR164 publication-title: Sci Rep doi: 10.1038/s41598-019-46718-3 – ident: 9648_CR167 doi: 10.1109/CSCS.2017.56 – ident: 9648_CR19 doi: 10.3390/app8122422 – volume: 146 start-page: 1765 issue: 7 year: 2020 ident: 9648_CR15 publication-title: J Cancer Res Clin Oncol doi: 10.1007/s00432-020-03232-8 – volume: 8 start-page: 674 issue: 7 year: 2019 ident: 9648_CR91 publication-title: Int J Sci Technol Res – ident: 9648_CR100 doi: 10.1109/ACCESS.2019.2904245 – volume: 13 start-page: 1 issue: 4 year: 2018 ident: 9648_CR127 publication-title: PLoS ONE doi: 10.1371/journal.pone.0195875 – volume: 1 start-page: 1 year: 2019 ident: 9648_CR135 publication-title: PeerJ doi: 10.7717/peerj.6201 – ident: 9648_CR97 doi: 10.1109/STSIVA.2019.8730284 – ident: 9648_CR46 doi: 10.1097/MOP.0b013e328343f4dd – volume: 7 start-page: 90132 year: 2019 ident: 9648_CR84 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2926837 – ident: 9648_CR12 doi: 10.1101/324541 – ident: 9648_CR34 doi: 10.1136/bmj.f4706 – year: 2020 ident: 9648_CR89 publication-title: J Cancer Res Clin Oncol doi: 10.1007/s00432-020-03291-x – volume: 8 start-page: 1402 issue: 2–3 year: 2019 ident: 9648_CR123 publication-title: Int J Recent Technol Eng – volume: 22 start-page: 191 year: 2009 ident: 9648_CR66 publication-title: Clinics in Colon – ident: 9648_CR28 doi: 10.1155/2014/842037 – volume: 18 start-page: 120 issue: 1 year: 2020 ident: 9648_CR73 publication-title: Current Treatment Options Gastroenterol doi: 10.1007/s11938-020-00274-2 – ident: 9648_CR87 doi: 10.17148/IJREEICE.2015.3229 – volume: 21 start-page: 1 issue: 1 year: 2021 ident: 9648_CR170 publication-title: BMC Med Imaging doi: 10.1186/s12880-021-00608-1 – ident: 9648_CR176 – volume: 8 start-page: 1088 year: 2015 ident: 9648_CR165 publication-title: Algorithms doi: 10.3390/a8041088 – ident: 9648_CR155 – ident: 9648_CR23 doi: 10.1109/BMEiCON.2018.8609997 – ident: 9648_CR60 doi: 10.1109/SampTA45681.2019.9030909 – volume: 2016 start-page: 3219068 year: 2016 ident: 9648_CR96 publication-title: Biomed Res Int doi: 10.1155/2016/3219068 – volume: 146 start-page: 809 issue: 4 year: 2020 ident: 9648_CR102 publication-title: J Cancer Res Clin Oncol doi: 10.1007/s00432-020-03172-3 – volume: 10 start-page: 1 year: 2020 ident: 9648_CR37 publication-title: Appl Sci doi: 10.3390/app10207292 – volume: 10 start-page: 720 year: 2020 ident: 9648_CR70 publication-title: Sci Rep doi: 10.1038/s41598-020-57670-y – volume: 21 start-page: 1 issue: 1 year: 2021 ident: 9648_CR157 publication-title: BMC Med Imaging doi: 10.1186/s12880-021-00563-x – volume: 56 start-page: 1 year: 2020 ident: 9648_CR86 publication-title: Jornal Brasileiro de Patologia e Medicina Laboratorial doi: 10.5935/1676-2444.20200013 – ident: 9648_CR160 doi: 10.1109/IST.2016.7738230 – ident: 9648_CR180 – volume: 24 start-page: 64 year: 2009 ident: 9648_CR119 publication-title: Oral Microbiol Immunol doi: 10.1111/j.1399-302X.2008.00479.x – volume: 119 start-page: 581 year: 2018 ident: 9648_CR128 publication-title: Int J Pure Appl Math – ident: 9648_CR45 doi: 10.1007/978-981-10-6890-4_11 – volume: 21 start-page: 1 issue: 1 year: 2021 ident: 9648_CR39 publication-title: BMC Med Imaging doi: 10.1186/s12880-021-00594-4 – volume: 2 start-page: 1 year: 2020 ident: 9648_CR156 publication-title: IEEE Xplorer – ident: 9648_CR108 doi: 10.1109/ICCUBEA.2018.8697804 – volume: 21 start-page: 1 issue: 1 year: 2021 ident: 9648_CR74 publication-title: BMC Med Imaging doi: 10.1186/s12880-021-00566-8 – volume: 142 start-page: 1807 issue: 8 year: 2016 ident: 9648_CR182 publication-title: J Cancer Res Clin Oncol doi: 10.1007/s00432-016-2131-6 – volume: 3 start-page: 1 year: 2020 ident: 9648_CR121 publication-title: Front Big Data doi: 10.3389/fdata.2020.00006 – volume: 20 start-page: 1 year: 2018 ident: 9648_CR13 publication-title: Global Trend Breast Cancer Mortal – year: 2020 ident: 9648_CR95 publication-title: J Cancer Res Clin Oncol doi: 10.1007/s00432-020-03318-3 – volume: 10 start-page: 1 year: 2020 ident: 9648_CR117 publication-title: Appl Sci doi: 10.3390/app10093134 – volume: 24 start-page: 729 year: 2010 ident: 9648_CR29 publication-title: Ann Oncol doi: 10.1093/annonc/mdp391 – year: 2011 ident: 9648_CR56 publication-title: Diagn Therap Endos doi: 10.1155/2011/182435 – year: 2020 ident: 9648_CR154 publication-title: Neural Comput Appl doi: 10.1007/s00521-020-04842-6 – volume: 2020 start-page: 1 year: 2020 ident: 9648_CR136 publication-title: J Healthc Eng doi: 10.1155/2020/9162464 – ident: 9648_CR21 doi: 10.1155/2013/942353 – ident: 9648_CR81 – year: 2019 ident: 9648_CR76 publication-title: PLoS ONE doi: 10.1371/journal.pone.0217293 – volume: 4 start-page: 75 issue: 8 year: 2017 ident: 9648_CR148 publication-title: Int J Res Sci Innov – volume: 146 start-page: 2339 issue: 9 year: 2020 ident: 9648_CR77 publication-title: J Cancer Res Clin Oncol doi: 10.1007/s00432-020-03304-9 – ident: 9648_CR126 – volume: 146 start-page: 2041 issue: 8 year: 2020 ident: 9648_CR53 publication-title: J Cancer Res Clin Oncol doi: 10.1007/s00432-020-03264-0 – volume: 25 start-page: 417 issue: 2 year: 2017 ident: 9648_CR152 publication-title: J Sci Res doi: 10.5829/idosi.mejsr.2017.417.426 – ident: 9648_CR115 doi: 10.2147/BCTT.S176070 – volume: 144 start-page: 799 issue: 5 year: 2018 ident: 9648_CR144 publication-title: J Cancer Res Clin Oncol doi: 10.1007/s00432-018-2595-7 – volume: 9 start-page: 1 year: 2009 ident: 9648_CR3 publication-title: BMC Med Res Methodol doi: 10.1186/1471-2288-9-74 – volume: 79 start-page: 7731 issue: 11–12 year: 2020 ident: 9648_CR22 publication-title: Multimed Tools Appl doi: 10.1007/s11042-019-08394-3 – volume: 18 start-page: 1 year: 2017 ident: 9648_CR178 publication-title: BMC Bioinform doi: 10.1186/s12859-017-1922-3 |
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| Snippet | Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to... |
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| SubjectTerms | Artificial intelligence Cancer Deep learning Engineering Health care Machine learning Mathematical and Computational Engineering Parameter sensitivity Review Review Article |
| Title | A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis |
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