A Survey of Existing Approaches for Computer Aided Diagnosis of Cancer
DOI:
https://doi.org/10.61841/4aa25y73Keywords:
Digital Pathology, Computer Aided Diagnosis (CAD), Artificial Intelligence (AI), Tumor, Staging and Grading, Histopathology, Gene Expression, Fuzzy, Neural Networks (FNN), Support Vector Machines (SVM), Adaptive Neuro- Fuzzy Interface System (ANFIS)Abstract
Cancer is a deadly disease in which abnormal cells divide uncontrollably and destroy body tissue. It has various diagnosis techniques which are limited to only to some types of cancer. These diagnosis techniques have limited efficiency and do not guarantee the patient’s life. We plan diagnosis of this deadly disease at the initial stage itself. We take the patient’s medical records and gene data and look for cancer causing mutations(driver) and separate the other mutations (passengers).Thus we classify the gene and detect the mutations so that any patient with harmful mutations are potentially risk patients and thus are given treatment.
Downloads
References
[1] M. Schena, D. Shalon, R.W. Davis, and P.O. Brown, “Quantitative Monitoring of Gene Expression Patterns
with a Complementary DNA Microarray,” Science, vol. 270, pp. 467-470, 1995.
[2] J.M. Khan et al., “Classification and Diagnostic Prediction of Cancers Using Gene Expression Profiling and
Artificial Neural Networks,” Nature Medicine, vol. 7, pp. 673- 679, 2001.
[3] J. Deutsch, “Evolutionary Algorithms for Finding Optimal Gene Sets in Microarray Prediction,”
Bioinformatics, vol. 19, pp. 4552, 2003.
[4] R. Tibshirani, T. Hastie, B. Narashiman, and G. Chu, “Diagnosis of Multiple Cancer Types by Shrunken
Centroids of Gene Expression,” Proc. Nat’l Academy of Sciences USA, vol. 99, pp. 6567-6572, 2002.
[5] M. P. Brown, W.N. Grundy, D. Lin, N. Cristianini, C.W. Sumet, T.S.F- . M. Jr. Ares. D. Haussler.
“Knowledge-based analysis of microarray gene expression data by using support vector machines,” Proc. N
dA c d Sei US.4,Vol. 97,pp 262-267.2000.
[6] Reta, C., et al. Segmentation of Bone Marrow Cell Images for Morphological Classification of Acute
Leukemia in FLAIRS Conference. 2010. USA: Abbrev of Publisher, year, ch. 4, sec. 21, pp. 142 –147.
[7] Demir, C. and B. Yener, Automated cancer diagnosis based on histopathological images: a systematic
survey. Rensselaer Polytechnic Institute, Tech. Rep, 2005, p. 232-237
[8] Jyh-Shing Roger Jang. “ANFIS: Adaptive Network Based Fuzzy Inference System”. IEEE Trans. on
System, Man and Cybernetics,vol. 23, no. 3. 1993
[9] O.L. Mangasarian, W.N. Street, and W.H. Wolberg, “Breast cancer diagnosis and prognosis via linear programming,” Mathematical Programming Technical Report 9410, University of Wisconsin, 1994.
[10] W. H. Land, Jr., T. Masters, and J. Y. Lo.“Application of a New Evolutionary Programming/Adaptive
Boosting Hybrid to Breast Cancer Diagnosis.” IEEE Congress on Evolutionary Computation Proceedings, 2000.
Downloads
Published
Issue
Section
License
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
