CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Early Detection and Diagnosis of Prostate Cancer using Artificial Intelligence Concept

by Onuiri Ernest E., Awodele Oludele, Ebiesuwa Oluwaseun
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 149 - Number 6
Year of Publication: 2016
Authors: Onuiri Ernest E., Awodele Oludele, Ebiesuwa Oluwaseun
10.5120/ijca2016911433

Onuiri Ernest E., Awodele Oludele, Ebiesuwa Oluwaseun . Early Detection and Diagnosis of Prostate Cancer using Artificial Intelligence Concept. International Journal of Computer Applications. 149, 6 ( Sep 2016), 42-46. DOI=10.5120/ijca2016911433

@article{ 10.5120/ijca2016911433,
author = { Onuiri Ernest E., Awodele Oludele, Ebiesuwa Oluwaseun },
title = { Early Detection and Diagnosis of Prostate Cancer using Artificial Intelligence Concept },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2016 },
volume = { 149 },
number = { 6 },
month = { Sep },
year = { 2016 },
issn = { 0975-8887 },
pages = { 42-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume149/number6/26005-2016911433/ },
doi = { 10.5120/ijca2016911433 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:54:03.173913+05:30
%A Onuiri Ernest E.
%A Awodele Oludele
%A Ebiesuwa Oluwaseun
%T Early Detection and Diagnosis of Prostate Cancer using Artificial Intelligence Concept
%J International Journal of Computer Applications
%@ 0975-8887
%V 149
%N 6
%P 42-46
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Contemporary research has shown that prostate cancer is the most common cancer amongst older men which can lead to death. However, it has been proven that early detection for most cancers is known to help in making therapeutic decisions in urologic medicine and oncology. Current methods of screening for prostate cancer carried out through blood PSA tests (presence of high Prostate Specific Antigen in the blood) and digital rectal examinations due to their morphological nature lead to a high percentage of False Positive Test Results (FPTR). The occurrence of this FPTR can be reduced by employing Artificial Intelligence (AI) techniques such as Artificial Neural Network (ANN) in evaluating the need for a patient to undergo biopsy. The goal of this research entails the proposition of a model for the early detection of prostate cancer to enhance early detection so as to enable early commencement of intervention to increase the chances of successful treatment and reduction in mortality rates. This research was predicated on the review and analysis of current AI paradigms tailored towards effective detection of prostate cancer. Research findings showed that the PSA test and Digital Rectal Examination (DRE) are not adequate enough to detect prostate cancer but the deployment of assay specific ANN model, which is a machine learning technique reduces the amount of unnecessary biopsies.

References
  1. Hruban, R. (2012). What are tumors? Retrieved 2015, from http://pathology.jhu.edu/
  2. National Cancer Institute. (2014). Prostate cancer.
  3. American cancer society. (2015). Prostate cancer prevention and early detection.
  4. Miller, D., Hafez , K., Stewart , A., Montie JE, J., & Wei , J. (2003). "Prostate carcinoma presentation, diagnosis, and staging: an update form the National Cancer Data Base".
  5. Hankey, B., Feuer, E., Clegg, L., Hayes , R., & Legler, J. (1999). Cancer surveillance series: interpreting trends in prostate cancer—part I: Evidence of the effects of screening in recent prostate cancer incidence, mortality, and survival rates.
  6. Brawer, M., Bartsch, G., D'Amico, A., Donohue, R., Siam, O., & Tewari, A. (2003). early diagnosis and stging of prostate cancer.
  7. Vogelzang, N., & Shore, N. (2015). upfront chemotherapy in prostate cancer. Retrieved 2015, from cancer network: http://www.cancernetwork.com/prostate-cancer/upfront-chemotherapy-prostate-cancer?
  8. Porter, C., & Crawford, D. (2003). combining artificial neural networks and transurethral ultrasound in the diagnosis of prostate cancer.
  9. Revett, k., Sergio Tenreiro de Magalhaes, & Santos, H. (n.d.). Data Mining a Prostate Cancer Dataset Using.
  10. Raza, K., & Hasan, A. (n.d.). A comprehensive evaluation of machin learning technique for cancer class prediction based on microarray data.
  11. Snow, P., Smith, D., & Catalona , W. (1994). Artificial neural networks in the diagnosis and prognosis of prostate cancer: A pilot study.
  12. Djavan, B., Remzi, M., & Zlotta, A. (2002). Novel artificial neural network for early detection of prostate cancer.
  13. Meyer, H. (2013). Artificial neural networks and prostate cancer-tools for diagnosis and management.
  14. Tsao, C., Liu, C., Cha, T., Wu, S., & Sun, G. (2014). Artificial neural network for predicting pathological stage of clinically localized prostate cancer in a Taiwanese population. Journal of the Chinese Medical Association 77.
  15. Barry, M. (2001). Prostate specific antigen testing.
  16. Schwarzzer, G., & Schumacher, M. (2002). Artificial neural networks for diagnosis and prognosis in prostate cancer.
  17. Anagnostou, T. (2003). Artificial neural networks for decision making in urologic oncology.
  18. Stephan, C., Jung, K., Lein, M., & Diamandis, E. (2007). PSA nad other tissue kalikreinsfor prostate cancer detection. Eur J Cancer.
  19. National Cancer Institute. (2011). Tumor marker sheet. Retrieved from www.cancer.gov: http://www.cancer.gov/about-cancer/diagnosis-staging/diagnosis/tumor-markers-fact-sheet
  20. Leman, E., Cannon, G., Trock, B., Sokoll, L., & Chan, D. (2007). EPCA -2 a highly specific serum marker for prostate cancer. Retrieved from www.ncbi.nlm: htttp://www.ncbi.nlm/pubmed
Index Terms

Computer Science
Information Sciences

Keywords

Prostate Cancer Artificial Intelligence Prostate Specific Antigen Digital Rectal Examination Biopsy.

Powered by PhDFocusTM