Lung Cancer Detection from X-ray images by combined Backpropagation Neural Network and PCA
Engineering and Technology Journal,
2019, Volume 37, Issue 5A, Pages 166-171
AbstractThe lungs are portion of a complex unit, enlarging and relaxing numerus times every day to supply oxygen and exude CO2. Lung disease might occur from troubles in any part of it. Carcinoma often called Cancer is the generally rising and it is the most harmful disease happened in humankind. Carcinoma occurs because of uncontrolled growth of malignant cells inside the tissues of the lungs. Earlier diagnosis of cancer can help save large numbers of lives, while any delay or fail in detection may cause additional serious problems leading to sudden fatal death. The objective of this study is to design an automated system with an ability to improve the detection process in order to perform advanced recognition of the disease. The diagnosis techniques include: X-rays, MRI, CT images etc. X-ray is the common and low-cost technique that is widely used and it is relatively available for everyone. Rather than new techniques like CT and MRI, X-ray is human dependable, meaning it needs a Doctor and X-ray specialist in order to determine lung cases, so developing a system which can enhance and aid in diagnosis, can help specialist to determine cases in easily.
 T. Kaur and N. Gupta, “Classifications of Lung
Diseases by uses Optimization Techniques,”
International Journal for Scientific Research and
Development (IJSRD), Vol.3, Issue.8, 2015.
 K. Balachandran, and R. Anitha, “An Efficients
Optimization Based upon Lung Cancer Prediagnosis
System with Aided of Feed Forward Back Propagation
Neural Network (FFBNN),” Journal of Theoretical and
Applied Information Technology, Vol.56 No.2, 2013.
 G.W. Staton and S. Bhalla, “Imaging of Lung
Disease,” BC Decker Inc., DOI 10.2310/7900.S14C02,
14 resp ii, 2008.
 K. Dimililer, Y.K. Ever and B. Ugur, “ILTDS:
Intelligent Lung Tumor Detection System on CT
Images,” Springer International Publishing, Intelligent
Systems Technologies and Applications, DOI
 U.I. Dike and U.A. Adoghe, “Computer-aided
diagnosis in medical imaging: Historical review,
current status and future potential,” International
Journal of Computers and Distributed Systems, Vol.
No.3, Issue 2, ISSN: 2278-5183, Jun-July 2013.
 P. Kemal and G. Salih, “Principles component
analysis, fuzzy weighting preprocessing and artificial
immune recognition system based diagnostic system for
diagnosis of lung cancer,” Expert Systems with
Applications, Vol.34, No. 1, pp. 214–221, 2008.
 Z.M. Abood, “Benign and Malignant of Breast
Tumors Classification by Backpropagation Neural
networks,” Iraqi Journal of Information Technology,
Vol. 6, Issue 2, pp.13-20, 2014.
 I.C. Mary, J. Preethi, “A Survey on Computerized,
Quantification and Classification of Lung Disease,”
 U. Javed, M. M. Riaz, T.A. Cheema and H.M.
Zafar, “Detection of Lung Tumor in CE CT Images by
using Weighted Support Vector Machines,” IEEE, pp.
 S.K. Devireddy and S.A. Rao, “Hand Written
Character Recognition Using Backpropagation
Network,” Journal of Theoretical and Applied
Information Technology (JATIT), 2009.
- Article View: 32
- PDF Download: 18