Generic placeholder image

Combinatorial Chemistry & High Throughput Screening

Editor-in-Chief

ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

Review Article

Role of Artificial Intelligence in Cancer Diagnosis and Drug Development

Author(s): Shubham Srivastava and Deepika Paliwal*

Volume 25, Issue 13, 2022

Published on: 12 April, 2022

Page: [2141 - 2152] Pages: 12

DOI: 10.2174/1386207325666220304112914

Price: $65

Abstract

Cancer is a vast form of the disease that can begin in almost any organ or tissue of the body when abnormal cells grow uncontrollably and attack nearby organs. The traditional approaches to cancer diagnosis and drug development have certain limitations, and the outcomes achieved through the traditional approaches applied to cancer diagnosis and drug development are not quite promising. Artificial intelligence is not new to the medical research sector. AI-based algorithms hold great potential for identifying mutations and abnormal cell division at the initial stage of cancer. Advanced researchers are also focusing on bringing AI to clinics in a safe and ethical manner. Early cancer detection saves lives and is critical in the fight against the disease. As a result, as part of earlier detection, computational approaches such as artificial intelligence have played a significant role in cancer diagnosis and drug development.

Keywords: Cancer, computational approach, artificial intelligence, machine learning, deep learning, drug development.

Graphical Abstract
[1]
Mak, L.; Liggi, S.; Tan, L.; Kusonmano, K.; Rollinger, J.M.; Koutsoukas, A.; Glen, R.C.; Kirchmair, J. Anti-cancer drug development: computational strategies to identify and target proteins involved in cancer metabolism. Curr. Pharm. Des., 2013, 19(4), 532-577.
[http://dx.doi.org/10.2174/138161213804581855] [PMID: 23016852]
[2]
Cui, W.; Aouidate, A.; Wang, S.; Yu, Q.; Li, Y.; Yuan, S. Discovering anti-cancer drugs via computational methods. Front. Pharmacol., 2020, 11, 733.
[http://dx.doi.org/10.3389/fphar.2020.00733] [PMID: 32508653]
[3]
Massard, C.; Michiels, S.; Ferté, C.; Le Deley, M.C.; Lacroix, L.; Hollebecque, A.; Verlingue, L.; Ileana, E.; Rosellini, S.; Ammari, S.; Ngo-Camus, M.; Bahleda, R.; Gazzah, A.; Varga, A.; Postel-Vinay, S.; Loriot, Y.; Even, C.; Breuskin, I.; Auger, N.; Job, B.; De Baere, T.; Deschamps, F.; Vielh, P.; Scoazec, J.Y.; Lazar, V.; Richon, C.; Ribrag, V.; Deutsch, E.; Angevin, E.; Vassal, G.; Eggermont, A.; André, F.; Soria, J.C. High-throughput genomics and clinical outcome in hard-to-treat advanced cancers: Results of the MOSCATO 01 trial. Cancer Discov., 2017, 7(6), 586-595.
[http://dx.doi.org/10.1158/2159-8290.CD-16-1396] [PMID: 28365644]
[4]
Meric-Bernstam, F.; Mills, G.B. Overcoming implementation challenges of personalized cancer therapy. Nat. Rev. Clin. Oncol., 2012, 9(9), 542-548.
[http://dx.doi.org/10.1038/nrclinonc.2012.127] [PMID: 22850751]
[5]
Flaherty, K.T.; Hodi, F.S.; Fisher, D.E. From genes to drugs: Targeted strategies for melanoma. Nat. Rev. Cancer, 2012, 12(5), 349-361.
[http://dx.doi.org/10.1038/nrc3218] [PMID: 22475929]
[6]
Higgins, M.J.; Baselga, J. Targeted therapies for breast cancer. J. Clin. Invest., 2011, 121(10), 3797-3803.
[http://dx.doi.org/10.1172/JCI57152] [PMID: 21965336]
[7]
Hanna, T.P.; Kangolle, A.C. Cancer control in developing countries: using health data and health services research to measure and improve access, quality and efficiency. BMC Int. Health Hum. Rights, 2010, 10(1), 24.
[http://dx.doi.org/10.1186/1472-698X-10-24] [PMID: 20942937]
[9]
Statistics, C. Cancer Statistics Available from: https://www. cancer.gov/about-cancer/understanding/statistics
[10]
Kapetanovic, I.M. Computer-aided drug discovery and development (CADDD): In silico-chemico-biological approach. Chem. Biol. Interact., 2008, 171(2), 165-176.
[http://dx.doi.org/10.1016/j.cbi.2006.12.006] [PMID: 17229415]
[11]
Prada-Gracia, D.; Huerta-Yépez, S.; Moreno-Vargas, L.M. Application of computational methods for anticancer drug discovery, design, and optimization. In: Boletín Médico Del Hosp. Infant. México (English Ed); , 2016; 73, pp. (6)411-423.
[http://dx.doi.org/10.1016/j.bmhime.2017.11.040]
[12]
Sudhakar, A. History of cancer, ancient and modern treatment methods. J. Cancer Sci. Ther., 2009, 1(2), 1-4.
[http://dx.doi.org/10.4172/1948-5956.100000e2.History] [PMID: 20740081]
[13]
Overington, J.P.; Al-Lazikani, B.; Hopkins, A.L. How many drug targets are there? Nat. Rev. Drug Discov., 2006, 5(12), 993-996.
[http://dx.doi.org/10.1038/nrd2199] [PMID: 17139284]
[14]
Svoboda, B. E. , 33208974.
[15]
Rajkomar, A.; Dean, J.; Kohane, I. Machine learning in medicine. N. Engl. J. Med., 2019, 380(14), 1347-1358.
[http://dx.doi.org/10.1056/NEJMra1814259] [PMID: 30943338]
[16]
Chan, H.C.S.; Shan, H.; Dahoun, T.; Vogel, H.; Yuan, S. Advancing drug discovery via artificial intelligence. Trends Pharmacol. Sci., 2019, 40(8), 592-604.
[http://dx.doi.org/10.1016/j.tips.2019.06.004] [PMID: 31320117]
[17]
Nitta, N.; Sugimura, T.; Isozaki, A.; Mikami, H.; Hiraki, K.; Sakuma, S.; Iino, T.; Arai, F.; Endo, T.; Fujiwaki, Y.; Fukuzawa, H.; Hase, M.; Hayakawa, T.; Hiramatsu, K.; Hoshino, Y.; Inaba, M.; Ito, T.; Karakawa, H.; Kasai, Y.; Koizumi, K.; Lee, S.; Lei, C.; Li, M.; Maeno, T.; Matsusaka, S.; Murakami, D.; Nakagawa, A.; Oguchi, Y.; Oikawa, M.; Ota, T.; Shiba, K.; Shintaku, H.; Shirasaki, Y.; Suga, K.; Suzuki, Y.; Suzuki, N.; Tanaka, Y.; Tezuka, H.; Toyokawa, C.; Yalikun, Y.; Yamada, M.; Yamagishi, M.; Yamano, T.; Yasumoto, A.; Yatomi, Y.; Yazawa, M.; Di Carlo, D.; Hosokawa, Y.; Uemura, S.; Ozeki, Y.; Goda, K. Intelligent image-activated cell sorting. Cell, 2018, 175(1), 266-276.e13.
[http://dx.doi.org/10.1016/j.cell.2018.08.028] [PMID: 30166209]
[18]
Tripathy, R.K.; Mahanta, S.; Paul, S. Artificial intelligence-based classification of breast cancer using cellular images. RSC Advances, 2014, 4(18), 9349-9355.
[http://dx.doi.org/10.1039/c3ra47489e]
[19]
Patel, S.K.; George, B.; Rai, V. Artificial intelligence to decode cancer mechanism: beyond patient stratification for precision oncology. Front. Pharmacol., 2020, 11, 1177.
[http://dx.doi.org/10.3389/fphar.2020.01177] [PMID: 32903628]
[20]
Shaikh, K.; Krishnan, S.; Thanki, R. Artificial Intelligence in breast cancer early detection and diagnosis; Switzerland: Springer Nature, 2021.
[http://dx.doi.org/10.1007/978-3-030-59208-0]
[21]
Institute, N.C. Institute, N. C. Artificial intelligence - opportunities in cancer research Available from: https://www.cancer.gov/research/areas/diagnosis/artificial-intelligence
[22]
Cancer, A.I. Contents., 2020, 3228(3), 45-50.
[23]
Mak, K.K.; Pichika, M.R. Artificial intelligence in drug development: Present status and future prospects. Drug Discov. Today, 2019, 24(3), 773-780.
[http://dx.doi.org/10.1016/j.drudis.2018.11.014] [PMID: 30472429]
[24]
Sun, X.; Young, J.; Liu, J.H.; Newman, D. Prediction of pork loin quality using online computer vision system and artificial intelligence model. Meat Sci., 2018, 140(2), 72-77.
[http://dx.doi.org/10.1016/j.meatsci.2018.03.005] [PMID: 29533814]
[25]
Sellwood, M.A.; Ahmed, M.; Segler, M.H.S.; Brown, N. Artificial intelligence in drug discovery. Future Med. Chem., 2018, 10(17), 2025-2028.
[http://dx.doi.org/10.4155/fmc-2018-0212] [PMID: 30101607]
[26]
Lind, A.P.; Anderson, P.C. Predicting drug activity against cancer cells by random forest models based on minimal genomic information and chemical properties. PLoS One, 2019, 14(7), e0219774.
[http://dx.doi.org/10.1371/journal.pone.0219774] [PMID: 31295321]
[27]
Hossain, M.A.; Saiful Islam, S.M.; Quinn, J.M.W.; Huq, F.; Moni, M.A. Machine learning and bioinformatics models to identify gene expression patterns of ovarian cancer associated with disease progression and mortality. J. Biomed. Inform., 2019, 100, 103313.
[http://dx.doi.org/10.1016/j.jbi.2019.103313] [PMID: 31655274]
[28]
Paik, E.S.; Lee, J.W.; Park, J.Y.; Kim, J.H.; Kim, M.; Kim, T.J.; Choi, C.H.; Kim, B.G.; Bae, D.S.; Seo, S.W. Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods. J. Gynecol. Oncol., 2019, 30(4), e65.
[http://dx.doi.org/10.3802/jgo.2019.30.e65] [PMID: 31074247]
[29]
McDonald, J.F. Back to the future - The integration of big data with machine learning is re-establishing the importance of predictive correlations in ovarian cancer diagnostics and therapeutics. Gynecol. Oncol., 2018, 149(2), 230-231.
[http://dx.doi.org/10.1016/j.ygyno.2018.03.053] [PMID: 29572028]
[30]
Li, Q.; Qi, L.; Feng, Q.X.; Liu, C.; Sun, S.W.; Zhang, J.; Yang, G.; Ge, Y.Q.; Zhang, Y.D.; Liu, X.S. Machine learning-based computational models derived from large-scale radiographic-radiomic images can help predict adverse histopathological status of gastric cancer. Clin. Transl. Gastroenterol., 2019, 10(10), e00079.
[http://dx.doi.org/10.14309/ctg.0000000000000079] [PMID: 31577560]
[31]
Taninaga, J.; Nishiyama, Y.; Fujibayashi, K.; Gunji, T.; Sasabe, N.; Iijima, K.; Naito, T. Prediction of future gastric cancer risk using a machine learning algorithm and comprehensive medical check-up data: A case-control study. Sci. Rep., 2019, 9(1), 12384.
[http://dx.doi.org/10.1038/s41598-019-48769-y] [PMID: 31455831]
[32]
Goldenberg, S.L.; Nir, G.; Salcudean, S.E. A new era: artificial intelligence and machine learning in prostate cancer. Nat. Rev. Urol., 2019, 16(7), 391-403.
[http://dx.doi.org/10.1038/s41585-019-0193-3] [PMID: 31092914]
[33]
Arnaldo, S.; Cuocolo, R.; Renata, D.G.; Anna, N.; Valeria, R.; Antonio, T.; Antonio, R.; Giuseppe, B.; Fulvio, Z.; Luigi, I.; Simone, M.; Mainenti, P.P. Deep myometrial infiltration of endometrial cancer on MRI: A radiomics-powered machine learning pilot study. Acad. Radiol., 2020, 28(5), 737-744.
[http://dx.doi.org/10.1016/j.acra.2020.02.028]
[34]
Günakan, E.; Atan, S.; Haberal, A.N.; Küçükyıldız, İ.A.; Gökçe, E.; Ayhan, A. A novel prediction method for lymph node involvement in endometrial cancer: Machine learning. Int. J. Gynecol. Cancer, 2019, 29(2), 320-324.
[http://dx.doi.org/10.1136/ijgc-2018-000033] [PMID: 30718313]
[35]
Ciallella, H.L.; Zhu, H. Advancing computational toxicology in the big data era by artificial intelligence: data-driven and mechanism-driven modeling for chemical toxicity. Chem. Res. Toxicol., 2019, 32(4), 536-547.
[http://dx.doi.org/10.1021/acs.chemrestox.8b00393] [PMID: 30907586]
[36]
Zhu, H. Big data and artificial intelligence modeling for drug discovery. Annu. Rev. Pharmacol. Toxicol., 2020, 60, 573-589.
[http://dx.doi.org/10.1146/annurev-pharmtox-010919-023324] [PMID: 31518513]
[37]
Réda, C.; Kaufmann, E.; Delahaye-Duriez, A. Machine learning applications in drug development. Comput. Struct. Biotechnol. J., 2019, 18(December), 241-252.
[http://dx.doi.org/10.1016/j.csbj.2019.12.006] [PMID: 33489002]
[38]
Brown, N.; Hirst, J. In Silico medicinal chemistry computational methods to support drug design ; Jonathan, Hirst, Ed.; Royal Society of Chemistry, 2015.
[39]
Pereira, J.C.; Caffarena, E.R.; Dos Santos, C.N. Boosting docking-based virtual screening with deep learning. J. Chem. Inf. Model., 2016, 56(12), 2495-2506.
[http://dx.doi.org/10.1021/acs.jcim.6b00355] [PMID: 28024405]
[40]
Paul, D.; Sanap, G.; Shenoy, S.; Kalyane, D.; Kalia, K.; Tekade, R.K. Artificial intelligence in drug discovery and development. Drug Discov. Today, 2021, 26(1), 80-93.
[http://dx.doi.org/10.1016/j.drudis.2020.10.010] [PMID: 33099022]
[41]
Awale, M.; Reymond, J.L. Polypharmacology Browser PPB2: Target prediction combining nearest neighbors with machine learning. J. Chem. Inf. Model., 2019, 59(1), 10-17.
[http://dx.doi.org/10.1021/acs.jcim.8b00524] [PMID: 30558418]
[42]
Durrant, J.D.; McCammon, J.A. NNScore 2.0: A neural-network receptor-ligand scoring function. J. Chem. Inf. Model., 2011, 51(11), 2897-2903.
[http://dx.doi.org/10.1021/ci2003889] [PMID: 22017367]
[43]
Olivecrona, M.; Blaschke, T.; Engkvist, O.; Chen, H. Molecular de-novo design through deep reinforcement learning. J. Cheminform., 2017, 9(1), 48.
[http://dx.doi.org/10.1186/s13321-017-0235-x] [PMID: 29086083]
[44]
Coley, C.W.; Rogers, L.; Green, W.H.; Jensen, K.F. SCScore: Synthetic complexity learned from a reaction corpus. J. Chem. Inf. Model., 2018, 58(2), 252-261.
[http://dx.doi.org/10.1021/acs.jcim.7b00622] [PMID: 29309147]
[45]
Huang, S.; Yang, J.; Fong, S.; Zhao, Q. Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer Lett., 2020, 471, 61-71.
[http://dx.doi.org/10.1016/j.canlet.2019.12.007] [PMID: 31830558]
[46]
Ziad Obermeyer, M.D. Predicting the future-Big data, machine learning, and clinical medicine. N. Engl. J. Med., 2016, 375(13), 1216-1219.
[47]
Castellino, R.A. Computer aided detection (CAD): An overview. Cancer Imaging, 2005, 5(1), 17-19.
[http://dx.doi.org/10.1102/1470-7330.2005.0018] [PMID: 16154813]
[48]
Xu, Y.; Hosny, A.; Zeleznik, R.; Parmar, C.; Coroller, T.; Franco, I.; Mak, R.H.; Aerts, H.J.W.L. Deep learning predicts lung cancer treatment response from serial medical imaging. Clin. Cancer Res., 2019, 25(11), 3266-3275.
[http://dx.doi.org/10.1158/1078-0432.CCR-18-2495] [PMID: 31010833]
[49]
Cruz, J.A.; Wishart, D.S. Applications of machine learning in cancer prediction and prognosis. Cancer Inform., 2007, 2, 59-77.
[http://dx.doi.org/10.1177/117693510600200030] [PMID: 19458758]
[50]
Portable document format (PDF) specifications. Available from: www.fda.gov
[51]
Rajoub, B. Supervised and unsupervised learning. Biomed. Signal Process. Artif. Intell. Healthc., 2020, 51-89.
[http://dx.doi.org/10.1016/B978-0-12-818946-7.00003-2]
[52]
Angehrn, Z.; Haldna, L.; Zandvliet, A.S.; Gil Berglund, E.; Zeeuw, J.; Amzal, B.; Cheung, S.Y.A.; Polasek, T.M.; Pfister, M.; Kerbusch, T.; Heckman, N.M. artificial intelligence and machine learning applied at the point of care. Front. Pharmacol., 2020, 11, 759.
[http://dx.doi.org/10.3389/fphar.2020.00759] [PMID: 32625083]
[53]
Berry, M.; Mohamed, A.; Yap, B.W. Supervised and Unsupervised Learning for Data Science, 1st ed; Springer, 2020.
[54]
Pillai, B.G.; Jeena Jecob, I.; Madhurya, J.A.; Saritha, A.K. Predicting the possibility of cancer with supervised learning algorithms. Int. J. Emerg. Trends Eng. Res., 2020, 8(9), 5177-5179.
[http://dx.doi.org/10.30534/ijeter/2020/47892020]
[55]
Peng, L.; Chen, W.; Zhou, W.; Li, F.; Yang, J.; Zhang, J. An immune-inspired semi-supervised algorithm for breast cancer diagnosis. Comput. Methods Programs Biomed., 2016, 134, 259-265.
[http://dx.doi.org/10.1016/j.cmpb.2016.07.020] [PMID: 27480748]
[56]
Yang, L.; Jin, R. Distance Metric Learning: A Comprehensive Survey; Michigan State Universiy, 2006, pp. 1-51.
[http://dx.doi.org/10.1073/pnas.0809777106]
[57]
Bharadwaj; Prakash, K.B.; Kanagachidambaresan, G.R. Pattern Recognition and Machine Learning; Springer: Cham, 2021.
[http://dx.doi.org/10.1007/978-3-030-57077-4_11]
[58]
AhmedMedjahed, S.; Ait Saadi, T.; Benyettou, A. Breast cancer diagnosis by using K-nearest neighbor with different distances and classification rules. Int. J. Comput. Appl., 2013, 62(1), 1-5.
[http://dx.doi.org/10.5120/10041-4635]
[59]
Khorshid, S. F.; Abdulazeez, A. M. Breast cancer diagnosis based on K-nearest neighbors: A review. PalArch’s J. Archaeol. Egypt/Egyptology, 2021, 18(4), 1927-1951.
[60]
Abreu, P.H.; Santos, M.S. Techniques : A systematic review. ACM Comput. Surv., 2016, 49(3), 1-40.
[http://dx.doi.org/10.1145/2988544]
[61]
Banu, A.B.; Thirumalaikolundusubramanian, P. Comparison of bayes classifiers for breast cancer classification. Asian Pac. J. Cancer Prev., 2018, 19(10), 2917-2920.
[http://dx.doi.org/10.22034/APJCP.2018.19.10.2917] [PMID: 30362322]
[62]
Liu, D.; Wang, S.; Huang, D.; Deng, G.; Zeng, F.; Chen, H. Medical image classification using spatial adjacent histogram based on adaptive local binary patterns. Comput. Biol. Med., 2016, 72, 185-200.
[http://dx.doi.org/10.1016/j.compbiomed.2016.03.010] [PMID: 27058283]
[63]
Jaganathan, R.; Ramasamy, V. Performance modeling of bio-inspired routing protocols in cognitive radio ad hoc network to reduce end-to-end delay. Int. J. Intell. Eng. Syst., 2019, 12(1), 221-231.
[http://dx.doi.org/10.22266/ijies2019.0228.22]
[64]
Lingaraj, M.; Senthilkumar, A.; Ramkumar, J. Prediction of melanoma skin cancer using veritable support vector machine. Ann. Rom. Soc. Cell Biol., 2021, 25(4), 2623-2636.
[65]
Manju, B.R.; Athira, V.; Rajendran, A. Efficient multi-level lung cancer prediction model using support vector machine classifier. IOP Conf. Ser. Mater. Sci. Eng., 2021, 1012, p. 012034.
[http://dx.doi.org/10.1088/1757-899X/1012/1/012034]
[66]
Pramanik, P.K.D.; Pal, S.; Mukhopadhyay, M.; Singh, S.P. Big Data Classification: Techniques and Tools. In: Deepak, G.; Nilanjan, D.; Applications of Big Data in Healthcare; Ashish, K., Ed.; New Jersey: Academic Press , 2021; pp. 1-43.
[http://dx.doi.org/10.1016/B978-0-12-820203-6.00002-3]
[67]
Kulkarni, A.; Shrestha, A. Multispectral image analysis using decision trees. Int. J. Adv. Comput. Sci. Appl., 2017, 8(6), 11-18.
[http://dx.doi.org/10.14569/IJACSA.2017.080602]
[68]
Tabrizchi, H.; Tabrizchi, M.; Tabrizchi, H. Breast cancer diagnosis using a multi-verse optimizer-based gradient boosting decision Tree. SN Appl. Sci., 2020, 2(4), 1-19.
[http://dx.doi.org/10.1007/s42452-020-2575-9]
[69]
Vlahou, A.; Schorge, J.O.; Gregory, B.W.; Coleman, R.L. Diagnosis of ovarian cancer using decision tree classification of mass spectral data. J. Biomed. Biotechnol., 2003, 2003(5), 308-314.
[http://dx.doi.org/10.1155/S1110724303210032] [PMID: 14688417]
[70]
Jackins, V.; Vimal, S.; Kaliappan, M.; Lee, M.Y. AI-based smart prediction of clinical disease using random forest classifier and naive bayes. J. Supercomput., 2021, 77(5), 5198-5219.
[http://dx.doi.org/10.1007/s11227-020-03481-x]
[71]
Acharya, U.R.; Ng, E.Y.K.; Tan, J.H.; Sree, S.V. Thermography based breast cancer detection using texture features and support vector machine. J. Med. Syst., 2012, 36(3), 1503-1510.
[http://dx.doi.org/10.1007/s10916-010-9611-z] [PMID: 20957511]
[72]
Maglogiannis, I.; Zafiropoulos, E.; Anagnostopoulos, I. An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers. Appl. Intell., 2009, 30(1), 24-36.
[http://dx.doi.org/10.1007/s10489-007-0073-z]
[73]
Huang, Y.L.; Wang, K.L.; Chen, D.R. Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines. Neural Comput. Appl., 2006, 15(2), 164-169.
[http://dx.doi.org/10.1007/s00521-005-0019-5]
[74]
Abbass, H.A. An evolutionary artificial neural networks approach for breast cancer diagnosis. Artif. Intell. Med., 2002, 25(3), 265-281.
[http://dx.doi.org/10.1016/S0933-3657(02)00028-3] [PMID: 12069763]
[75]
Tourassi, G.D.; Markey, M.K.; Lo, J.Y.; Floyd, C.E. Jr A neural network approach to breast cancer diagnosis as a constraint satisfaction problem. Med. Phys., 2001, 28(5), 804-811.
[http://dx.doi.org/10.1118/1.1367861] [PMID: 11393476]
[76]
Karabatak, M. A new classifier for breast cancer detection based on naïve bayesian. Meas. J. Int. Meas. Confed., 2015, 72, 32-36.
[http://dx.doi.org/10.1016/j.measurement.2015.04.028]
[77]
Şahan, S.; Polat, K.; Kodaz, H.; Güneş, S. A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis. Comput. Biol. Med., 2007, 37(3), 415-423.
[http://dx.doi.org/10.1016/j.compbiomed.2006.05.003] [PMID: 16904096]
[78]
Bagui, S.C.; Bagui, S.; Pal, K.; Pal, N.R. Breast cancer detection using rank nearest neighbor classification rules. Pattern Recognit., 2003, 36(1), 25-34.
[http://dx.doi.org/10.1016/S0031-3203(02)00044-4]
[79]
Nardini, C. Machine learning in oncology: A review. Ecancermedicalscience, 2020, 14, 1065.
[http://dx.doi.org/10.3332/ecancer.2020.1065] [PMID: 32728381]
[80]
Dey, A. Machine learning algorithms: A review. Int. J. Comput. Sci. Inf. Technol., 2016, 7(3), 1174-1179.
[81]
Shalev-Shwartz, S.; Singer, Y.; Srebro, N.; Cotter, A. Pegasos: Primal estimated sub-gradient solver for SVM. Math. Program., 2011, 127(1), 3-30.
[http://dx.doi.org/10.1007/s10107-010-0420-4]
[82]
Filipczuk, P.; Kowal, M.; Obuchowicz, A. Fuzzy clustering and adaptive thresholding based segmentation method for breast cancer diagnosis. Adv. Intell. Soft Comput., 2011, 95(4), 613-622.
[http://dx.doi.org/10.1007/978-3-642-20320-6_64]
[83]
R, U.M. An efficient cancer classification using mid value KMeans and Naïve Bayes. J. Sci. Comput. Eng. Res., 2020, 1-6.
[http://dx.doi.org/10.46379/jscer.2020.010101]
[84]
Lin, H.; Ji, Z. Breast cancer prediction based on k-means and SOM hybrid algorithm. J. Phys. Conf. Ser., 2020, 1624(4), 04212.
[http://dx.doi.org/10.1088/1742-6596/1624/4/042012]
[85]
Guo, Y.; Gao, Y.; Shen, D.; Deformable, M.R. Deformable MR prostate segmentation via deep feature learning and sparse patch matching. IEEE Trans. Med. Imaging, 2016, 35(4), 1077-1089.
[http://dx.doi.org/10.1109/TMI.2015.2508280] [PMID: 26685226]
[86]
Kleppe, A.; Skrede, O-J.; De Raedt, S.; Liestøl, K. Designing deep learning studies in cancer diagnostics. Nat. Rev. Cancer, 2021, 2021(March), 199-211.
[87]
Munir, K.; Elahi, H.; Ayub, A.; Frezza, F.; Rizzi, A. Cancer diagnosis using deep learning: a bibliographic review. Cancers (Basel), 2019, 11(9), 1-36.
[http://dx.doi.org/10.3390/cancers11091235] [PMID: 31450799]
[88]
Ehteshami Bejnordi, B.; Veta, M.; Johannes van Diest, P.; van Ginneken, B.; Karssemeijer, N.; Litjens, G.; van der Laak, J.A.W.M.; Hermsen, M.; Manson, Q.F.; Balkenhol, M.; Geessink, O.; Stathonikos, N.; van Dijk, M.C.R.F.; Bult, P.; Beca, F.; Beck, A.H.; Wang, D.; Khosla, A.; Gargeya, R.; Irshad, H.; Zhong, A.; Dou, Q.; Li, Q.; Chen, H.; Lin, H.J.; Heng, P.A.; Haß, C.; Bruni, E.; Wong, Q.; Halici, U.; Öner, M.Ü.; Cetin-Atalay, R.; Berseth, M.; Khvatkov, V.; Vylegzhanin, A.; Kraus, O.; Shaban, M.; Rajpoot, N.; Awan, R.; Sirinukunwattana, K.; Qaiser, T.; Tsang, Y.W.; Tellez, D.; Annuscheit, J.; Hufnagl, P.; Valkonen, M.; Kartasalo, K.; Latonen, L.; Ruusuvuori, P.; Liimatainen, K.; Albarqouni, S.; Mungal, B.; George, A.; Demirci, S.; Navab, N.; Watanabe, S.; Seno, S.; Takenaka, Y.; Matsuda, H.; Ahmady Phoulady, H.; Kovalev, V.; Kalinovsky, A.; Liauchuk, V.; Bueno, G.; Fernandez-Carrobles, M.M.; Serrano, I.; Deniz, O.; Racoceanu, D.; Venâncio, R. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA, 2017, 318(22), 2199-2210.
[http://dx.doi.org/10.1001/jama.2017.14585] [PMID: 29234806]
[89]
Zhu, X.; Yao, J.; Huang, J. Deep convolutional neural network for survival analysis with pathological images.Proc. - 2016 IEEE Int. Conf. Bioinforma. Biomed. BIBM; , 2017, 1, pp. 544-547.
[http://dx.doi.org/10.1109/BIBM.2016.7822579]
[90]
Paul, R.; Hawkins, S.H.; Hall, L.O.; Goldgof, D.B.; Gillies, R.J. Combining deep neural network and traditional image features to improve survival prediction accuracy for lung cancer patients from diagnostic CT. 2016 IEEE Int. Conf. Syst. Man, Cybern. SMC 2016 - Conf. Proc., 2017, pp. 2570-2575.
[http://dx.doi.org/10.1109/SMC.2016.7844626]
[91]
Tan, Y.J.; Sim, K.S.; Ting, F.F. Breast cancer detection using convolutional neural networks for mammogram imaging system. Proceeding 2017 Int. Conf. Robot. Autom. Sci. ICORAS 2017, 2018, pp. 1-5.
[http://dx.doi.org/10.1109/ICORAS.2017.8308076]
[92]
Byra, M.; Piotrzkowska-Wroblewska, H.; Dobruch-Sobczak, K.; Nowicki, A. Combining nakagami imaging and convolutional neural network for breast lesion classification. IEEE Int. Ultrason. Symp. IUS, 2017, pp. 5-8.
[http://dx.doi.org/10.1109/ULTSYM.2017.8092154]
[93]
Gao, F.; Wu, T.; Li, J.; Zheng, B.; Ruan, L.; Shang, D.; Patel, B.S.D-C.N.N. A shallow-deep CNN for improved breast cancer diagnosis. Comput. Med. Imaging Graph., 2018, 70, 53-62.
[http://dx.doi.org/10.1016/j.compmedimag.2018.09.004] [PMID: 30292910]
[94]
Wang, Z.; Li, M.; Wang, H.; Jiang, H.; Yao, Y.; Zhang, H.; Xin, J. Breast cancer detection using extreme learning machine based on feature fusion with CNN deep features. IEEE Access, 2019, 7(c), 105146-105158.
[http://dx.doi.org/10.1109/ACCESS.2019.2892795]
[95]
Ragab, D.A.; Sharkas, M.; Marshall, S.; Ren, J. Breast cancer detection using deep convolutional neural networks and support vector machines. PeerJ, 2019, 7(1), e6201.
[http://dx.doi.org/10.7717/peerj.6201] [PMID: 30713814]
[96]
Karthik, S.; Srinivasa Perumal, R.; Chandra Mouli, P.V.S.S.R. Breast cancer classification using deep neural networks. Knowl. Comput. Its Appl. Knowl. Manip. Process. Tech., 2018, 1, 227-241.
[http://dx.doi.org/10.1007/978-981-10-6680-1_12]
[97]
De Yu, S.; Liu, L.L.; Wang, Z.Y.; Dai, G.Z.; Xie, Y.Q. Transferring deep neural networks for the differentiation of mammographic breast lesions. Sci. China Technol. Sci., 2019, 62(3), 441-447.
[http://dx.doi.org/10.1007/s11431-017-9317-3]
[98]
Shen, W.; Zhou, M.; Yang, F.; Yu, D.; Dong, D.; Yang, C.; Zang, Y.; Tian, J. Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recognit., 2017, 61, 663-673.
[http://dx.doi.org/10.1016/j.patcog.2016.05.029]
[99]
Pereira, S.; Pinto, A.; Alves, V.; Silva, C.A. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging, 2016, 35(5), 1240-1251.
[http://dx.doi.org/10.1109/TMI.2016.2538465] [PMID: 26960222]
[100]
Ji, D.; Yu, J.; Kurihara, T.; Xu, L.; Zhan, S. Automatic prostate segmentation on MR images with deeply supervised network. 2018 5th Int. Conf. Control. Decis. Inf. Technol. CoDIT 2018,; , 2018, pp. 309-314.
[http://dx.doi.org/10.1109/CoDIT.2018.8394836]
[101]
Tian, Z.; Liu, L.; Zhang, Z.; Fei, B. PSNet: prostate segmentation on MRI based on a convolutional neural network. J. Med. Imaging (Bellingham), 2018, 5(2), 021208.
[http://dx.doi.org/10.1117/1.JMI.5.2.021208] [PMID: 29376105]
[102]
Pacal, I.; Karaboga, D.; Basturk, A.; Akay, B.; Nalbantoglu, U. A comprehensive review of deep learning in colon cancer. Comput. Biol. Med., 2020, 126, 104003.
[http://dx.doi.org/10.1016/j.compbiomed.2020.104003] [PMID: 32987202]
[103]
Bilal, A.M.; Raza, S.E.A.; Azam, A.; Graham, S.; Cree, I.A.; Snead, D.; Minhas, F.; Rajpoot, N.M.; Ilyas, M. Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study. Lancet Digit. Health, 2021, 3(12), e763-e772.
[104]
Ozawa, T.; Ishihara, S.; Fujishiro, M.; Kumagai, Y.; Shichijo, S.; Tada, T. Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks. Therap. Adv. Gastroenterol., 2020, 13, 1756284820910659.
[http://dx.doi.org/10.1177/1756284820910659] [PMID: 32231710]
[105]
Kayser, M.; Soberanis-Mukul, R. D.; Zvereva, A.-M.; Klare, P.; Navab, N.; Albarqouni, S. Understanding the effects of artifacts on automated polyp detection and incorporating that knowledge via learning without forgetting, 2020.
[106]
Zeng, Y.; Xu, S.; Chapman, W.C., Jr; Li, S.; Alipour, Z.; Abdelal, H.; Chatterjee, D.; Mutch, M.; Zhu, Q. Real-time colorectal cancer diagnosis using PR-OCT with deep learning. Theranostics, 2020, 10(6), 2587-2596.
[http://dx.doi.org/10.7150/thno.40099] [PMID: 32194821]
[107]
Javed, S.; Mahmood, A.; Fraz, M.M.; Koohbanani, N.A.; Benes, K.; Tsang, Y.W.; Hewitt, K.; Epstein, D.; Snead, D.; Rajpoot, N. Cellular community detection for tissue phenotyping in colorectal cancer histology images. Med. Image Anal., 2020, 63, 101696.
[http://dx.doi.org/10.1016/j.media.2020.101696] [PMID: 32330851]
[108]
Saito, H.; Aoki, T.; Aoyama, K.; Kato, Y.; Tsuboi, A.; Yamada, A.; Fujishiro, M.; Oka, S.; Ishihara, S.; Matsuda, T.; Nakahori, M.; Tanaka, S.; Koike, K.; Tada, T. Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network. Gastrointest. Endosc., 2020, 92(1), 144-151.e1.
[http://dx.doi.org/10.1016/j.gie.2020.01.054] [PMID: 32084410]
[109]
Qadir, H.A.; Balasingham, I.; Solhusvik, J.; Bergsland, J.; Aabakken, L.; Shin, Y. Improving automatic polyp detection using CNN by exploiting temporal dependency in colonoscopy video. IEEE J. Biomed. Health Inform., 2020, 24(1), 180-193.
[http://dx.doi.org/10.1109/JBHI.2019.2907434] [PMID: 30946683]
[110]
Wang, D.; Zhang, N.; Sun, X.; Zhang, P.; Zhang, C.; Cao, Y.; Liu, B. AFP-Net: Realtime anchor-free polyp detection in colonoscopy. Proc. - Int. Conf. Tools with Artif. Intell; , 2019, 2019, pp. 636-643.
[http://dx.doi.org/10.1109/ICTAI.2019.00094]
[111]
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.
[http://dx.doi.org/10.1016/j.compeleceng.2019.106531]
[112]
Yuan, Y.; Qin, W.; Ibragimov, B.; Zhang, G.; Han, B.; Meng, M.Q.H.; Xing, L. Densely connected neural network with unbalanced discriminant and category sensitive constraints for polyp recognition. IEEE Trans. Autom. Sci. Eng., 2020, 17(2), 574-583.
[http://dx.doi.org/10.1109/TASE.2019.2936645]
[113]
Taqdir, A. Cancer Detection Techniques - a Review. Intern. Res. J. Engin. Technol., 2018, 5(4), 1834.
[114]
Esteva, A.; Kuprel, B.; Novoa, R.A.; Ko, J.; Swetter, S.M.; Blau, H.M.; Thrun, S. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017, 542(7639), 115-118.
[http://dx.doi.org/10.1038/nature21056] [PMID: 28117445]
[115]
Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., 2015, pp. 1-14.
[116]
Mahbod, A.; Schaefer, G.; Wang, C.; Ecker, R.; Ellinger, I. Tissue Gnostics GmbH; 1229-1233. Austria Department of Computer Science, Loughborough University: UK. Department of Biomedical. , 2019.
[117]
Yu, L.; Chen, H.; Dou, Q.; Qin, J.; Heng, P.A. Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans. Med. Imaging, 2017, 36(4), 994-1004.
[http://dx.doi.org/10.1109/TMI.2016.2642839] [PMID: 28026754]
[118]
Xue, W.; Hu, X. jiao; Wei, Z.; Mei, X. lan; Chen, X. jian; Xu, Y. chun. Prediction of Compost Maturity Based on Convolutional Neural Network; Springer international publishing, 2019, p. 25.
[http://dx.doi.org/10.11674/zwyf.18477]

Rights & Permissions Print Export Cite as
© 2023 Bentham Science Publishers | Privacy Policy