CoChat
Sign Up
Cited by 70AI in cancer detectionRadiomics and Machine Learning in Medical ImagingMedical Image Segmentation Techniques

Deep learning in selected cancers’ image analysis—a survey

Open access
Debelee, Taye Girma, Kebede, Samuel Rahimeto, Schwenker, Friedhelm, Shewarega, Zemene Matewos·2020·Journal of Imaging
DOI 10.3390/jimaging6110121PMC PMC8321208
Open this paper in CoChat — free account

Abstract

Deep learning algorithms have become the first choice as an approach to medical image analysis, face recognition, and emotion recognition. In this survey, several deep-learning-based approaches applied to breast cancer, cervical cancer, brain tumor, colon and lung cancers are studied and reviewed. Deep learning has been applied in almost all of the imaging modalities used for cervical and breast cancers and MRIs for the brain tumor. The result of the review process indicated that deep learning methods have achieved state-of-the-art in tumor detection, segmentation, feature extraction and classification. As presented in this paper, the deep learning approaches were used in three different modes that include training from scratch, transfer learning through freezing some layers of the deep learning network and modifying the architecture to reduce the number of parameters existing in the network. Moreover, the application of deep learning to imaging devices for the detection of various cancer cases has been studied by researchers affiliated to academic and medical institutes in economically developed countries; while, the study has not had much attention in Africa despite the dramatic soar of cancer risks in the continent.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/jimaging6110121

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • AI has become a critical adjunct for medical image analytics, with computer models achieving state-of-the-art tumor detection, segmentation, extraction, and classification across a wide range of cancer types.

    “For example, AI has rapidly become a critical adjunct for medical image analytics, demonstrating that computer models can achieve state-of-the-art tumor detection, segmentation, extraction, and classification across a wide range of cancer types [9].”

    From Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice (2026) · cites this paper as [9]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 132

  1. [2]Shen L. Margolies L.R. Rothstein J.H. Fluder E. McBride R. Sieh W. Deep Learning to Improve Breast Cancer Detection on Screening Mammography Sci. Rep. 2019 9 10.1038/s41598-019-48995-4 31467326 PMC6715802 · DOI
  2. [4]Litjens G. Kooi T. Bejnordi B.E. Setio A.A.A. Ciompi F. Ghafoorian M. van der Laak J.A.W.M. van Ginneken B. Sánchez C.I. A survey on deep learning in medical image analysis Med. Image Anal. 2017 42 60 88 10.1016/j.media.2017.07.005 28778026 · DOI · Paper page
  3. [5]Shen D. Wu G. Suk H.I. Deep Learning in Medical Image Analysis Annu. Rev. Biomed. Eng. 2017 19 221 248 10.1146/annurev-bioeng-071516-044442 28301734 PMC5479722 · DOI · Paper page
  4. [6]Suzuki K. Overview of deep learning in medical imaging Radiol. Phys. Technol. 2017 10 257 273 10.1007/s12194-017-0406-5 28689314 · DOI
  5. [7]Lee J. Jun S. Cho Y. Lee H. Kim G.B. Seo J.B. Kim N. Deep Learning in Medical Imaging: General Overview Korean J. Radiol. 2017 18 570 584 10.3348/kjr.2017.18.4.570 28670152 PMC5447633 · DOI
  6. [8]Debelee T.G. Schwenker1 F. Rahimeto S. Yohannes D. Evaluation of modified adaptive k-means segmentation algorithm Comput. Vis. Media 2019 10.1007/s41095-019-0151-2 · DOI
  7. [10]Rahimeto S. Debelee T. Yohannes D. Schwenker F. Automatic pectoral muscle removal in mammograms Evol. Syst. 2019 10.1007/s12530-019-09310-8 · DOI
  8. [11]Debelee T.G. Schwenker F. Ibenthal A. Yohannes D. Survey of deep learning in breast cancer image analysis Evol. Syst. 2019 10.1007/s12530-019-09297-2 · DOI
  9. [12]Debelee T.G. Gebreselasie A. Schwenker F. Amirian M. Yohannes D. Classification of Mammograms Using Texture and CNN Based Extracted Features J. Biomim. Biomater. Biomed. Eng. 2019 42 79 97 10.4028/www.scientific.net/JBBBE.42.79 · DOI
  10. [16]Moreira I.C. Amaral I. Domingues I. Cardoso A. Cardoso M.J. Cardoso J.S. INbreast Acad. Radiol. 2012 19 236 248 10.1016/j.acra.2011.09.014 22078258 · DOI
  11. [19]Aresta G. Araújo T. Kwok S. Chennamsetty S.S. Safwan M. Alex V. Marami B. Prastawa M. Chan M. Donovan M. BACH: Grand challenge on breast cancer histology images Med. Image Anal. 2019 56 122 139 10.1016/j.media.2019.05.010 31226662 · DOI
  12. [20]Wu N. Phang J. Park J. Shen Y. Huang Z. Zorin M. Jastrzebski S. Fevry T. Katsnelson J. Kim E. Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening IEEE Trans. Med. Imaging 2020 39 1184 1194 10.1109/TMI.2019.2945514 31603772 PMC7427471 · DOI
  13. [22]Zhu Z. Harowicz M. Zhang J. Saha A. Grimm L.J. Hwang E.S. Mazurowski M.A. Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ Comput. Biol. Med. 2019 115 103498 10.1016/j.compbiomed.2019.103498 31698241 · DOI
  14. [23]Li X. Qin G. He Q. Sun L. Zeng H. He Z. Chen W. Zhen X. Zhou L. Digital breast tomosynthesis versus digital mammography: Integration of image modalities enhances deep learning-based breast mass classification Eur. Radiol. 2019 30 778 788 10.1007/s00330-019-06457-5 31691121 · DOI
  15. [24]Zeiser F.A. da Costa C.A. Zonta T. Marques N.M.C. Roehe A.V. Moreno M. da Rosa Righi R. Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning J. Digit. Imaging 2020 10.1007/s10278-020-00330-4 PMC7522129 32206943 · DOI · Paper page
  16. [25]Zhang Y. Chen J.H. Chang K.T. Park V.Y. Kim M.J. Chan S. Chang P. Chow D. Luk A. Kwong T. Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net Acad. Radiol. 2019 26 1526 1535 10.1016/j.acra.2019.01.012 30713130 PMC6669125 · DOI
  17. [26]Zhou J. Luo L.Y. Dou Q. Chen H. Chen C. Li G.J. Jiang Z.F. Heng P.A. Weakly supervised 3D deep learning for breast cancer classification and localization of the lesions in MR images J. Magn. Reson. Imaging 2019 50 1144 1151 10.1002/jmri.26721 30924997 · DOI
  18. [27]Huang G. Liu Z. Maaten L.V.D. Weinberger K.Q. Densely Connected Convolutional Networks Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Honolulu, HI, USA 21–26 July 2017 10.1109/cvpr.2017.243 · DOI · Paper page
  19. [28]Zhang J. Saha A. Soher B.J. Mazurowski M.A. Automatic deep learning-based normalization of breast dynamic contrast-enhanced magnetic resonance images arXiv 2018 1807.02152v1
  20. [29]Sheikh T.S. Lee Y. Cho M. Histopathological Classification of Breast Cancer Images Using a Multi-Scale Input and Multi-Feature Network Cancers 2020 12 2031 10.3390/cancers12082031 PMC7465368 32722111 · DOI
  21. [30]Li X. Shen X. Zhou Y. Wang X. Li T.Q. Classification of breast cancer histopathological images using interleaved DenseNet with SENet (IDSNet) PLoS ONE 2020 15 e0232127 10.1371/journal.pone.0232127 32365142 PMC7198071 · DOI
  22. [31]Yan R. Ren F. Wang Z. Wang L. Zhang T. Liu Y. Rao X. Zheng C. Zhang F. Breast cancer histopathological image classification using a hybrid deep neural network Methods 2020 173 52 60 10.1016/j.ymeth.2019.06.014 31212016 · DOI
  23. [32]Sharma S. Mehra R. Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images—a Comparative Insight J. Digit. Imaging 2020 33 632 654 10.1007/s10278-019-00307-y 31900812 PMC7256154 · DOI
  24. [33]Vang Y.S. Chen Z. Xie X. Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification Lecture Notes in Computer Science Springer International Publishing Berlin/Heidelberg, Germany 2018 914 922 10.1007/978-3-319-93000-8_104 · DOI
  25. [34]Dembrower K. Liu Y. Azizpour H. Eklund M. Smith K. Lindholm P. Strand F. Comparison of a Deep Learning Risk Score and Standard Mammographic Density Score for Breast Cancer Risk Prediction Radiology 2020 294 265 272 10.1148/radiol.2019190872 31845842 · DOI
  26. [36]Ferlay J. Shin H.R. Bray F. Forman D. Mathers C. Parkin D.M. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008 Int. J. Cancer 2010 127 2893 2917 10.1002/ijc.25516 21351269 · DOI
  27. [37]Wingo P.A. Cardinez C.J. Landis S.H. Greenlee R.T. Ries L.A.G. Anderson R.N. Thun M.J. Long-term trends in cancer mortality in the United States, 1930–1998 Cancer 2003 97 3133 3275 10.1002/cncr.11380 12784323 · DOI
  28. [38]Schiffman M. Doorbar J. Wentzensen N. De Sanjosé S. Fakhry C. Monk B.J. Stanley M.A. Franceschi S. Carcinogenic human papillomavirus infection Nat. Rev. Dis. Prim. 2016 2 1 20 10.1038/nrdp.2016.86 27905473 · DOI · Paper page
  29. [39]Patten J. Comprehensive Cervical Cancer Control: A Guide to Essential Practice World Health Organization Geneva, Switzerland 2014 25642554
  30. [40]GençTav A. Aksoy S. ÖNder S. Unsupervised segmentation andclassification of cervical cell images Pattern Recognit. 2012 45 4151 4168 10.1016/j.patcog.2012.05.006 · DOI
  31. [41]Tang G.Y. Parekh J. Methods of Screening for Cervical Cancer JAMA 2018 320 1938 10.1001/jama.2018.13939 30422188 · DOI
  32. [42]Liang Y. Tang Z. Yan M. Chen J. Liu Q. Xiang Y. Comparison-Based Convolutional Neural Networks for Cervical Cell/ClumpsDetection in the Limited Data Scenario arXiv 2019 1810.05952
  33. [43]Jantzen J. Norup J. Dounias G. Bjerregaard B. Pap-smear benchmark data for pattern classification Proceedings of the Nature Inspired Smart Information Systems (NiSIS 2005) Albufeira, Portugal 3–5 October 2005
  34. [44]Hussain E. Liquid based cytology pap smear images for multi-class diagnosis of cervical cancer Data Brief 2019 10.17632/ZDDTPGZV63.4 PMC7186519 32368601 · DOI
  35. [47]Group A. Human Papillomavirus Testing for Triage of Women With Cytologic Evidence of Low-Grade Squamous Intraepithelial Lesions: Baseline Data From a Randomized Trial J. Natl. Cancer Inst. 2000 92 397 402 10.1093/jnci/92.5.397 10700419 · DOI
  36. [49]Song Y. Cheng J.Z. Ni D. Chen S. Lei B. Wang T. Segmenting overlapping cervical cell in Pap smear images Proceedings of the 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) Prague, Czech Republic 13–16 April 2016 10.1109/isbi.2016.7493472 · DOI
  37. [50]Zhao J. Dai L. Zhang M. Yu F. Li M. Li H. Wang W. Zhang L. PGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation Multiscale Multimodal Medical Imaging Springer International Publishing Berlin/Heidelberg, Germany 2019 51 58 10.1007/978-3-030-37969-8_7 · DOI
  38. [52]Sompawong N. Mopan J. Pooprasert P. Himakhun W. Suwannarurk K. Ngamvirojcharoen J. Vachiramon T. Tantibundhit C. Automated Pap Smear Cervical Cancer Screening Using Deep Learning Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Berlin, Germany 23–27 July 2019 10.1109/embc.2019.8856369 31947460 · DOI
  39. [53]Liu Y. Zhang P. Song Q. Li A. Zhang P. Gui Z. Automatic Segmentation of Cervical Nuclei Based on Deep Learning and a Conditional Random Field IEEE Access 2018 6 53709 53721 10.1109/ACCESS.2018.2871153 · DOI
  40. [54]Kurnianingsih A.K.H.S. Nugroho L.E. Widyawan Lazuardi L. Prabuwono A.S. Mantoro T. Segmentation and Classification of Cervical Cells Using Deep Learning IEEE Access 2019 7 116925 116941 10.1109/ACCESS.2019.2936017 · DOI
  41. [55]Zhang L. Lu L. Nogues I. Summers R.M. Liu S. Yao J. DeepPap: Deep Convolutional Networks for Cervical Cell Classification IEEE J. Biomed. Health Inform. 2017 21 1633 1643 10.1109/JBHI.2017.2705583 28541229 · DOI
  42. [56]Hyeon J. Choi H.J. Lee K.N. Lee B.D. Automating Papanicolaou Test Using Deep Convolutional Activation Feature Proceedings of the 2017 18th IEEE International Conference on Mobile Data Management (MDM) Daejeon, Korea 29 May–1 June 2017 10.1109/mdm.2017.66 · DOI
  43. [57]Lin H. Hu Y. Chen S. Yao J. Zhang L. Fine-Grained Classification of Cervical Cells Using Morphological and Appearance Based Convolutional Neural Networks IEEE Access 2019 7 71541 71549 10.1109/ACCESS.2019.2919390 · DOI
  44. [58]Deng J. Dong W. Socher R. Li L.J. Li K. Fei-Fei L. Imagenet: A large-scale hierarchical image database Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition Miami, FL, USA 20–25 June 2009 248 255
  45. [59]Nayar R. Wilbur D.C. The Pap test and Bethesda 2014 Cancer Cytopathol. 2015 123 271 281 10.1002/cncy.21521 25931431 · DOI · Paper page
  46. [60]Chen T. Ma X. Ying X. Wang W. Yuan C. Lu W. Chen D.Z. Wu J. Multi-Modal Fusion Learning For Cervical Dysplasia Diagnosis Proceedings of the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Venice, Italy 8–11 April 2019 10.1109/isbi.2019.8759303 · DOI
  47. [61]Promworn Y. Pattanasak S. Pintavirooj C. Piyawattanametha W. Comparisons of PAP-Smear Classification with Deep Learning Models Proceedings of the 14th annual IEEE International Conference on Nano/Micro Engineering and Molecular Systems Bangkok, Thailand 11–14 April 2019
  48. [62]Ma Y. Xu T. Huang X. Wang X. Li C. Jerwick J. Ning Y. Zeng X. Wang B. Wang Y. Computer-Aided Diagnosis of Label-Free 3-D Optical Coherence Microscopy Images of Human Cervical Tissue IEEE Trans. Biomed. Eng. 2019 66 2447 2456 10.1109/TBME.2018.2890167 30605087 PMC6724217 · DOI
  49. [63]Ghoneim A. Muhammad G. Hossain M.S. Cervical cancer classification using convolutional neural networks and extreme learning machines Future Gener. Comput. Syst. 2020 102 643 649 10.1016/j.future.2019.09.015 · DOI
  50. [64]Dong N. Zhao L. Wu C. Chang J. Inception v3 based cervical cell classification combined with artificially extracted features Appl. Soft Comput. 2020 93 106311 10.1016/j.asoc.2020.106311 · DOI
  51. [65]Martínez-Más J. Bueno-Crespo A. Martínez-España R. Remezal-Solano M. Ortiz-González A. Ortiz-Reina S. Martínez-Cendán J.P. Classifying Papanicolaou cervical smears through a cell merger approach by deep learning technique Expert Syst. Appl. 2020 160 113707 10.1016/j.eswa.2020.113707 · DOI
  52. [66]Xiang Y. Sun W. Pan C. Yan M. Yin Z. Liang Y. A novel automation-assisted cervical cancer reading method based on convolutional neural network Biocybern. Biomed. Eng. 2020 40 611 623 10.1016/j.bbe.2020.01.016 · DOI
  53. [67]Garcia‑Arteaga J. Kybic J. Li W. Automatic colposcopyvideo tissue classification using higher order entropy-basedimage registration Comput. Biol. Med. 2011 41 960 970 10.1016/j.compbiomed.2011.07.010 21890126 · DOI
  54. [68]Wimpy B. Suyanto S. Classification of Cervical and Type Image and Using and Capsule Networks Proceedings of the 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Yogyakarta, Indonesia 5–6 December 2019
  55. [69]Gorantla R. Singh R.K. Pandey R. Jain M. Cervical Cancer Diagnosis using CervixNet—A Deep Learning Approach Proceedings of the 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) Athens, Greece 28–30 October 2019 10.1109/bibe.2019.00078 · DOI
  56. [70]Arora M. Dhawan S. Singh K. Deep Neural Network for Transformation Zone Classification Proceedings of the 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC) Jalandhar, India 15–17 December 2018
  57. [72]Guo P. Xue Z. Long L.R. Antani S. Cross-Dataset Evaluation of Deep Learning Networks for Uterine Cervix Segmentation Diagnostics 2020 10 44 10.3390/diagnostics10010044 PMC7167955 31947707 · DOI
  58. [73]Guo P. Singh S. Xue Z. Long R. Antani S. Deep Learning for Assessing Image Focus for Automated Cervical Cancer Screening Proceedings of the 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) Chicago, IL, USA 19–22 May 2019 10.1109/bhi.2019.8834495 · DOI
  59. [74]Guo P. Xue Z. Mtema Z. Yeates K. Ginsburg O. Demarco M. Long L.R. Schiffman M. Antani S. Ensemble Deep Learning for Cervix Image Selection toward Improving Reliability in Automated Cervical Precancer Screening Diagnostics 2020 10 451 10.3390/diagnostics10070451 32635269 PMC7400120 · DOI
  60. [75]Sharma M. Mukharjee S. Brain Tumor Segmentation Using Genetic Algorithm and Artificial Neural Network Fuzzy Inference System (ANFIS) Advances in Computing and Information Technology Springer Berlin/Heidelberg, Germany 2013 329 339 10.1007/978-3-642-31552-7_35 · DOI
  61. [76]Hanif F. Muzaffar K. Perveen K. Malhi S.M. Simjee S.U. Glioblastoma Multiforme: A Review of its Epidemiology and Pathogenesis through Clinical Presentation and Treatment Asian Pac. J. Cancer Prev. APJCP 2017 18 3 9 10.22034/APJCP.2017.18.1.3 28239999 PMC5563115 · DOI
  62. [77]Rees J.H. Diagnosis and treatment in neuro-oncology: An oncological perspective Br. J. Radiol. 2011 84 S82 S89 10.1259/bjr/18061999 22433832 PMC3473904 · DOI
  63. [78]Rasmussen B.K. Hansen S. Laursen R.J. Kosteljanetz M. Schultz H. Nørgård B.M. Guldberg R. Gradel K.O. Epidemiology of glioma: Clinical characteristics, symptoms, and predictors of glioma patients grade I–IV in the the Danish Neuro-Oncology Registry J. Neuro Oncol. 2017 135 571 579 10.1007/s11060-017-2607-5 28861666 · DOI
  64. [79]Kalpathy-Cramer J. Gerstner E.R. Emblem K.E. Andronesi O. Rosen B. Advanced magnetic resonance imaging of the physical processes in human glioblastoma Cancer Res. 2014 74 4622 4637 10.1158/0008-5472.CAN-14-0383 25183787 PMC4155518 · DOI
  65. [80]Mohsen H. El-Dahshan E.S.A. El-Horbaty E.S.M. Salem A.B.M. Classification using deep learning neural networks for brain tumors Future Comput. Inform. J. 2018 3 68 71 10.1016/j.fcij.2017.12.001 · DOI
  66. [81]Bahadure N.B. Ray A.K. Thethi H.P. Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM Int. J. Biomed. Imaging 2017 2017 1 12 10.1155/2017/9749108 PMC5358478 28367213 · DOI
  67. [82]Srinivas B. Rao G.S. Segmentation of Multi-Modal MRI Brain Tumor Sub-Regions Using Deep Learning J. Electr. Eng. Technol. 2020 15 1899 1909 10.1007/s42835-020-00448-z · DOI
  68. [83]Nadeem M.W. Ghamdi M.A.A. Hussain M. Khan M.A. Khan K.M. Almotiri S.H. Butt S.A. Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges Brain Sci. 2020 10 118 10.3390/brainsci10020118 PMC7071415 32098333 · DOI
  69. [84]Akkus Z. Galimzianova A. Hoogi A. Rubin D.L. Erickson B.J. Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions J. Digit. Imaging 2017 30 449 459 10.1007/s10278-017-9983-4 28577131 PMC5537095 · DOI · Paper page
  70. [85]Shattuck D.W. Prasad G. Mirza M. Narr K.L. Toga A.W. Online resource for validation of brain segmentation methods NeuroImage 2009 45 431 439 10.1016/j.neuroimage.2008.10.066 19073267 PMC2757629 · DOI
  71. [86]Menze B.H. Jakab A. Bauer S. Kalpathy-Cramer J. Farahani K. Kirby J. Burren Y. Porz N. Slotboom J. Wiest R. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) IEEE Trans. Med. Imaging 2015 34 1993 2024 10.1109/TMI.2014.2377694 25494501 PMC4833122 · DOI · Paper page
  72. [87]Barboriak D. Data From RIDER_NEURO_MRI 2015 Available online: https://wiki.cancerimagingarchive.net/display/Public/RIDER+NEURO+MRI (accessed on 15 August 2020) 10.7937/K9/TCIA.2015.VOSN3HN1 · DOI
  73. [88]Fabelo H. Ortega S. Szolna A. Bulters D. Pineiro J.F. Kabwama S. J-O’Shanahan A. Bulstrode H. Bisshopp S. Kiran B.R. In-Vivo Hyperspectral Human Brain Image Database for Brain Cancer Detection IEEE Access 2019 7 39098 39116 10.1109/ACCESS.2019.2904788 · DOI
  74. [91]Alkassar S. Abdullah M.A.M. Jebur B.A. Automatic Brain Tumour Segmentation using fully Convolution Network and Transfer Learning Proceedings of the 2019 2nd International Conference on Electrical, Communication, Computer, Power and Control Engineering (ICECCPCE) Mosul, Iraq 13–14 February 2019 10.1109/iceccpce46549.2019.203771 · DOI
  75. [92]Amiri S. Rekik I. Mahjoub M.A. Deep random forest-based learning transfer to SVM for brain tumor segmentation Proceedings of the 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Monastir, Tunisia 21–23 March 2016 10.1109/atsip.2016.7523095 · DOI
  76. [93]Chahal E.S. Haritosh A. Gupta A. Gupta K. Sinha A. Deep Learning Model for Brain Tumor Segmentation & Analysis Proceedings of the 2019 3rd International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE) Noida, India 10–11 October 2019 10.1109/rdcape47089.2019.8979076 · DOI
  77. [94]Ding Y. Li C. Yang Q. Qin Z. Qin Z. How to Improve the Deep Residual Network to Segment Multi-Modal Brain Tumor Images IEEE Access 2019 7 152821 152831 10.1109/ACCESS.2019.2948120 · DOI
  78. [95]Mallick P.K. Ryu S.H. Satapathy S.K. Mishra S. Nguyen G.N. Tiwari P. Brain MRI Image Classification for Cancer Detection Using Deep Wavelet Autoencoder-Based Deep Neural Network IEEE Access 2019 7 46278 46287 10.1109/ACCESS.2019.2902252 · DOI
  79. [96]Ramirez I. Martin A. Schiavi E. Optimization of a variational model using deep learning: An application to brain tumor segmentation Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) Washington, DC, USA 4–7 April 2018 10.1109/isbi.2018.8363654 · DOI
  80. [97]Sajid S. Hussain S. Sarwar A. Brain Tumor Detection and Segmentation in MR Images Using Deep Learning Arab. J. Sci. Eng. 2019 44 9249 9261 10.1007/s13369-019-03967-8 · DOI
  81. [98]Wang Y. Li C. Zhu T. Yu C. A Deep Learning Algorithm for Fully Automatic Brain Tumor Segmentation Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN) Budapest, Hungary 14–19 July 2019 10.1109/ijcnn.2019.8852210 · DOI
  82. [99]Zhao X. Wu Y. Song G. Li Z. Zhang Y. Fan Y. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation Med. Image Anal. 2018 43 98 111 10.1016/j.media.2017.10.002 29040911 PMC6029627 · DOI · Paper page
  83. [100]Kuzina A. Egorov E. Burnaev E. Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems Front. Neurosci. 2019 13 10.3389/fnins.2019.00844 31496928 PMC6712162 · DOI
  84. [101]Zeineldin R.A. Karar M.E. Coburger J. Wirtz C.R. Burgert O. DeepSeg: Deep neural network framework for automatic brain tumor segmentation using magnetic resonance FLAIR images Int. J. Comput. Assist. Radiol. Surg. 2020 15 909 920 10.1007/s11548-020-02186-z 32372386 PMC7303084 · DOI
  85. [102]Fabelo H. Halicek M. Ortega S. Shahedi M. Szolna A. Piñeiro J. Sosa C. O’Shanahan A. Bisshopp S. Espino C. Deep Learning-Based Framework for In Vivo Identification of Glioblastoma Tumor using Hyperspectral Images of Human Brain Sensors 2019 19 920 10.3390/s19040920 30813245 PMC6412736 · DOI
  86. [103]Shree N.V. Kumar T.N.R. Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network Brain Inform. 2018 5 23 30 10.1007/s40708-017-0075-5 29313301 PMC5893499 · DOI
  87. [104]Tandel G.S. Biswas M. Kakde O.G. Tiwari A. Suri H.S. Turk M. Laird J. Asare C. Ankrah A.A. Khanna N.N. A Review on a Deep Learning Perspective in Brain Cancer Classification Cancers 2019 11 111 10.3390/cancers11010111 30669406 PMC6356431 · DOI
  88. [105]Alqudah A.M. Brain Tumor Classification Using Deep Learning Technique—A Comparison between Cropped, Uncropped, and Segmented Lesion Images with Different Sizes Int. J. Adv. Trends Comput. Sci. Eng. 2019 8 3684 3691 10.30534/ijatcse/2019/155862019 · DOI
  89. [106]Ucuzal H. Yasar S. Colak C. Classification of brain tumor types by deep learning with convolutional neural network on magnetic resonance images using a developed web-based interface Proceedings of the 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) Ankara Turkey 11–13 October 2019 10.1109/ismsit.2019.8932761 · DOI
  90. [107]Bazin P. Example MP2RAGEME Data Set 2019 Available online: https://uvaauas.figshare.com/articles/Example_MP2RAGEME_data_set/7938326 (accessed on 15 August 2020) 10.21942/uva.7938326.v1 · DOI
  91. [109]Selvy P.T. Dharani V.P. Indhuja A. Brain Tumour Detection Using Deep Learning Techniques Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. 2019 169 175 10.32628/CSEIT195233 · DOI
  92. [110]Sultan H.H. Salem N.M. Al-Atabany W. Multi-Classification of Brain Tumor Images Using Deep Neural Network IEEE Access 2019 7 69215 69225 10.1109/ACCESS.2019.2919122 · DOI · Paper page
  93. [111]Badža M.M. Barjaktarović M.Č. Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network Appl. Sci. 2020 10 1999 10.3390/app10061999 · DOI
  94. [113]Graham S. Chen H. Gamper J. Dou Q. Heng P.A. Snead D. Tsang Y.W. Rajpoot N. MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images Med. Image Anal. 2018 10.1016/j.media.2018.12.001 30594772 · DOI
  95. [114]Benson V.S. Patnick J. Davies A.K. Nadel M.R. Smith R.A. Atkin W.S. Colorectal cancer screening: A comparison of 35 initiatives in 17 countries Int. J. Cancer 2007 122 1357 1367 10.1002/ijc.23273 18033685 · DOI
  96. [115]Rony J. Belharbi S. Dolz J. Ayed I.B. McCaffrey L. Granger E. Deep weakly-supervised learning methods for classification and localization in histology images: A survey arXiv 2019 1909.03354v3
  97. [116]Vázquez D. Bernal J. Sánchez F.J. Fernández-Esparrach G. López A.M. Romero A. Drozdzal M. Courville A. A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images J. Healthc. Eng. 2017 2017 1 9 10.1155/2017/4037190 PMC5549472 29065595 · DOI
  98. [117]Bernal J. Sánchez J. Vilariño F. Towards automatic polyp detection with a polyp appearance model Pattern Recognit. 2012 45 3166 3182 10.1016/j.patcog.2012.03.002 · DOI
  99. [118]Bernal J. Sánchez F.J. Fernández-Esparrach G. Gil D. Rodríguez C. Vilariño F. WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians Comput. Med. Imaging Graph. 2015 43 99 111 10.1016/j.compmedimag.2015.02.007 25863519 · DOI · Paper page
  100. [119]Kather J.N. Weis C.A. Bianconi F. Melchers S.M. Schad L.R. Gaiser T. Marx A. Zöllner F.G. Multi-class texture analysis in colorectal cancer histology Sci. Rep. 2016 6 10.1038/srep27988 27306927 PMC4910082 · DOI · Paper page
  101. [120]Kainz P. Pfeiffer M. Urschler M. Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization PeerJ 2017 5 e3874 10.7717/peerj.3874 29018612 PMC5629961 · DOI
  102. [121]Sirinukunwattana K. Ahmed Raza S.E. Tsang Y.W. Snead D.R.J. Cree I.A. Rajpoot N.M. Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images IEEE Trans. Med. Imaging 2016 35 1196 1206 10.1109/TMI.2016.2525803 26863654 · DOI · Paper page
  103. [122]Chamanzar A. Nie Y. Weakly Supervised Multi-Task Learning for Cell Detection and Segmentation Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) Iowa City, IA, USA 4 April 2020 10.1109/isbi45749.2020.9098518 · DOI
  104. [123]Sari C.T. Gunduz-Demir C. Unsupervised Feature Extraction via Deep Learning for Histopathological Classification of Colon Tissue Images IEEE Trans. Med. Imaging 2019 38 1139 1149 10.1109/TMI.2018.2879369 30403624 · DOI
  105. [124]Ozdemir E. Gunduz-Demir C. A Hybrid Classification Model for Digital Pathology Using Structural and Statistical Pattern Recognition IEEE Trans. Med. Imaging 2013 32 474 483 10.1109/TMI.2012.2230186 23204283 · DOI
  106. [125]Shapcott M. Hewitt K.J. Rajpoot N. Deep Learning With Sampling in Colon Cancer Histology Front. Bioeng. Biotechnol. 2019 7 52 10.3389/fbioe.2019.00052 30972333 PMC6445856 · DOI
  107. [126]Tang J. Li J. Xu X. Segnet-based gland segmentation from colon cancer histology images Proceedings of the 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC) Nanjing, China 18 May 2018 10.1109/yac.2018.8406531 · DOI
  108. [127]Vuong T.L.T. Lee D. Kwak J.T. Kim K. Multi-task Deep Learning for Colon Cancer Grading Proceedings of the 2020 International Conference on Electronics, Information, and Communication (ICEIC) Fukuoka, Kapan 5–7 September 2020 10.1109/iceic49074.2020.9051305 · DOI
  109. [128]Sabol P. Sinčák P. Hartono P. Kočan P. Benetinová Z. Blichárová A. Verbóová Ľ. Štammová E. Sabolová-Fabianová A. Jašková A. Explainable classifier for improving the accountability in decision-making for colorectal cancer diagnosis from histopathological images J. Biomed. Inform. 2020 109 103523 10.1016/j.jbi.2020.103523 32758538 · DOI
  110. [129]Suzuki K. Yoshida H. Näppi J. Dachman A.H. Massive-training artificial neural network (MTANN) for reduction of false positives in computer-aided detection of polyps: Suppression of rectal tubes Med. Phys. 2006 33 3814 3824 10.1118/1.2349839 17089846 · DOI
  111. [130]Bardhi O. Sierra-Sosa D. Garcia-Zapirain B. Elmaghraby A. Automatic colon polyp detection using Convolutional encoder-decoder model Proceedings of the 2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) Bilbao, Spain 18–20 December 2017 10.1109/isspit.2017.8388684 · DOI
  112. [131]Bour A. Castillo-Olea C. Garcia-Zapirain B. Zahia S. Automatic colon polyp classification using Convolutional Neural Network: A Case Study at Basque Country Proceedings of the 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) Ajman, UAE 10–12 December 2019 10.1109/isspit47144.2019.9001816 · DOI
  113. [132]Liu X. Li Y. Yao J. Chen B. Song J. Yang X. Classification of Polyps and Adenomas Using Deep Learning Model in Screening Colonoscopy Proceedings of the 2019 8th International Symposium on Next Generation Electronics (ISNE) Zhengzhou, China 9–10 October 2019 10.1109/isne.2019.8896649 · DOI
  114. [133]Ozawa T. Ishihara S. Fujishiro M. Kumagai Y. Shichijo S. Tada T. Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks Ther. Adv. Gastroenterol. 2020 13 1756284820910659 10.1177/1756284820910659 PMC7092386 32231710 · DOI
  115. [134]Nadimi E.S. Buijs M.M. Herp J. Kroijer R. Kobaek-Larsen M. Nielsen E. Pedersen C.D. Blanes-Vidal V. Baatrup G. Application of deep learning for autonomous detection and localization of colorectal polyps in wireless colon capsule endoscopy Comput. Electr. Eng. 2020 81 106531 10.1016/j.compeleceng.2019.106531 · DOI
  116. [135]Zeiler M.D. Fergus R. Visualizing and Understanding Convolutional Networks Computer Vision—ECCV 2014 Springer International Publishing Berlin/Heidelberg, Germany 2014 818 833 10.1007/978-3-319-10590-1_53 · DOI · Paper page
  117. [137]Hong Z.Q. Yang J.Y. Optimal discriminant plane for a small number of samples and design method of classifier on the plane Pattern Recognit. 1991 24 317 324 10.1016/0031-3203(91)90074-F · DOI
  118. [138]Armato S.G. III Hadjiiski L. Tourassi G.D. Drukker K. Giger M.L. Li F. Redmond G. Farahani K. Kirby J.S. Clarke L.P. SPIE-AAPM-NCI Lung Nodule Classification Challenge Dataset Cancer Imaging Arch. 2015 10.7937/K9/TCIA.2015.UZLSU3FL PMC4489507 26158094 · DOI
  119. [140]Suzuki K. Armato S.G. Li F. Sone S. Doi K. Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography Med. Phys. 2003 30 1602 1617 10.1118/1.1580485 12906178 · DOI
  120. [141]Suzuki K. Li F. Sone S. Doi K. Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network IEEE Trans. Med. Imaging 2005 24 1138 1150 10.1109/TMI.2005.852048 16156352 · DOI
  121. [142]Tajbakhsh N. Suzuki K. Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs Pattern Recognit. 2017 63 476 486 10.1016/j.patcog.2016.09.029 · DOI
  122. [143]Gu Y. Lu X. Yang L. Zhang B. Yu D. Zhao Y. Gao L. Wu L. Zhou T. Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs Comput. Biol. Med. 2018 103 220 231 10.1016/j.compbiomed.2018.10.011 30390571 · DOI
  123. [144]Sahu P. Yu D. Dasari M. Hou F. Qin H. A Lightweight Multi-Section CNN for Lung Nodule Classification and Malignancy Estimation IEEE J. Biomed. Health Inform. 2019 23 960 968 10.1109/JBHI.2018.2879834 30418891 · DOI
  124. [145]Ozdemir O. Russell R.L. Berlin A.A. A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans IEEE Trans. Med. Imaging 2020 39 1419 1429 10.1109/TMI.2019.2947595 31675322 · DOI
  125. [147]Bansal G. Chamola V. Narang P. Kumar S. Raman S. Deep3DSCan: Deep residual network and morphological descriptor based framework for lung cancer classification and 3D segmentation IET Image Process. 2020 14 1240 1247 10.1049/iet-ipr.2019.1164 · DOI
  126. [148]Adegun A.A. Viriri S. Deep Learning-Based System for Automatic Melanoma Detection IEEE Access 2020 8 7160 7172 10.1109/ACCESS.2019.2962812 · DOI
  127. [149]Azer S.A. Challenges Facing the Detection of Colonic Polyps: What Can Deep Learning Do? Medicina 2019 55 473 10.3390/medicina55080473 PMC6723854 31409050 · DOI
  128. [150]Hamm C.A. Wang C.J. Savic L.J. Ferrante M. Schobert I. Schlachter T. Lin M. Duncan J.S. Weinreb J.C. Chapiro J. Deep learning for liver tumor diagnosis part I: Development of a convolutional neural network classifier for multi-phasic MRI Eur. Radiol. 2019 29 3338 3347 10.1007/s00330-019-06205-9 31016442 PMC7251621 · DOI
  129. [151]Yamakawa M. Shiina T. Nishida N. Kudo M. Computer aided diagnosis system developed for ultrasound diagnosis of liver lesions using deep learning Proceedings of the 2019 IEEE International Ultrasonics Symposium (IUS) Glasgow, UK 6–9 October 2019 2330 2333
  130. [152]Kim G.H. Liang P.S. Bang S.J. Hwang J.H. Screening and surveillance for gastric cancer in the United States: Is it needed? Gastrointest. Endosc. 2016 84 18 28 10.1016/j.gie.2016.02.028 26940296 · DOI
  131. [153]Lee J.H. Kim Y.J. Kim Y.W. Park S. Choi Y.I. Kim Y.J. Park D.K. Kim K.G. Chung J.W. Spotting malignancies from gastric endoscopic images using deep learning Surg. Endosc. 2019 33 3790 3797 10.1007/s00464-019-06677-2 30719560 · DOI
  132. [154]Cho B.J. Bang C.S. Park S.W. Yang Y.J. Seo S.I. Lim H. Shin W.G. Hong J.T. Yoo Y.T. Hong S.H. Automated classification of gastric neoplasms in endoscopic images using a convolutional neural network Endoscopy 2019 51 1121 1129 10.1055/a-0981-6133 31443108 · DOI

Cited by

Sorted by citations

Authors
Debelee, T. G., Kebede, S. R., Schwenker, F., & Shewarega, Z. M.
Affiliations
  • Addis Ababa Science and Technology University (ET)
  • Debre Berhan University (ET)
  • Universität Ulm (DE)
  • Artificial Intelligence Center, 40782 Addis Ababa, Ethiopia
Corresponding author
Debelee, Taye Girma
ORCID
Venue
Journal of Imaging
Publisher
MDPI AG
Published
2020-11-10
Volume
6
Issue
11
Pages
121
ISSN
2313-433X (ISSN-L)
DOI
10.3390/jimaging6110121
PMC
PMC8321208
OpenAlex
W3106310598
Cited by
70
References
4
Citations by year
2026: 8 · 2025: 8 · 2024: 7 · 2023: 18 · 2022: 16 · 2021: 13
FWCI
0.40
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Imaging (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • OPen Access Repositorium der Universität Ulm (OPARU) (Ulm University) (submittedVersion, cc-by)
  • Universität Ulm, Kommunikations- und Informationszentrum (kiz) (cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Keywords
deep learning, medical image analysis, cancer detection, breast cancer, cervical cancer, brain tumors, colon cancer, lung cancer, tumor segmentation, transfer learning, feature extraction, cancer classification, Africa

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review