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Research Article
Cancer Diagnosis Using Machine Learning and Image Recognition
Muneesh Pal1
Ahmed Ali Baig2
Sahil Shikalgar3
Shaikh Abdul Masood4
Momin Sumaan5
1Professor, Department of Information Technology Engineering, Armiet, Maharashtra, India. 2345Department of Information Technology Engineering, Armiet, Maharashtra, India.
Published Online: March-April 2023
Pages: 529-533
Cite this article
No DOIReferences
1. D. Hanahan, R.A. Weinberg Hallmarks of cancer: the next generation Cell, 144 (2011), pp. 646-674
2. M.-Y.C. Polley, B. Freidlin, E.L. Korn, B.A. Conley, J.S. Abrams, L.M. McShane Statistical and practical considerations for
clinical evaluation of predictive biomarkers J Natl Cancer Inst, 105 (2013), pp. 1677-1683
3. J.A. Cruz, D.S. Wishart Applications of machine learning in cancer prediction and prognosis Cancer Informant, 2(2006), p. 59
4. O. Fortunato, M. Boeri, C. Verri, D. Conte, M. Mensah, P. Suatoni, et al. Assessment of circulating microRNAs in plasma of lung
cancer patients Molecules, 19 (2014), pp. 3038-3054
5. L. Ein-Dor, I. Kela, G. Getz, D. Givol, E. Domany Outcome signature genes in breast cancer: is there a unique set?Bioinformatics, 21
(2005), pp. 171-178
6. L. Ein-Dor, O. Zuk, E. Domany Thousands of samples are needed to generate a robust gene list for predictingoutcome in cancer
Proc Natl Acad Sci, 103 (2006), pp. 5923-5928
7. T. Ayer, O. Alagoz, J. Chhatwal, J.W. Shavlik, C.E. Kahn, E.S. Burnside Breast cancer risk estimation with artificial neural networks
revisited Cancer, 116 (2010), pp. 3310-3321
8. J.C. Platt, N. Cristianini, J. Shawe-Taylor Large margin DAGs for multiclass classification (1999), pp. 547-553
9. D. Cicchetti Neural networks and diagnosis in the clinical laboratory: state of the art Clin Chem, 38 (1992), pp. 9-10
10. A.J. Cochran Prediction of outcome for patients with cutaneous melanoma Pigment Cell Res, 10 (1997), pp. 162-167
2. M.-Y.C. Polley, B. Freidlin, E.L. Korn, B.A. Conley, J.S. Abrams, L.M. McShane Statistical and practical considerations for
clinical evaluation of predictive biomarkers J Natl Cancer Inst, 105 (2013), pp. 1677-1683
3. J.A. Cruz, D.S. Wishart Applications of machine learning in cancer prediction and prognosis Cancer Informant, 2(2006), p. 59
4. O. Fortunato, M. Boeri, C. Verri, D. Conte, M. Mensah, P. Suatoni, et al. Assessment of circulating microRNAs in plasma of lung
cancer patients Molecules, 19 (2014), pp. 3038-3054
5. L. Ein-Dor, I. Kela, G. Getz, D. Givol, E. Domany Outcome signature genes in breast cancer: is there a unique set?Bioinformatics, 21
(2005), pp. 171-178
6. L. Ein-Dor, O. Zuk, E. Domany Thousands of samples are needed to generate a robust gene list for predictingoutcome in cancer
Proc Natl Acad Sci, 103 (2006), pp. 5923-5928
7. T. Ayer, O. Alagoz, J. Chhatwal, J.W. Shavlik, C.E. Kahn, E.S. Burnside Breast cancer risk estimation with artificial neural networks
revisited Cancer, 116 (2010), pp. 3310-3321
8. J.C. Platt, N. Cristianini, J. Shawe-Taylor Large margin DAGs for multiclass classification (1999), pp. 547-553
9. D. Cicchetti Neural networks and diagnosis in the clinical laboratory: state of the art Clin Chem, 38 (1992), pp. 9-10
10. A.J. Cochran Prediction of outcome for patients with cutaneous melanoma Pigment Cell Res, 10 (1997), pp. 162-167
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