Total views : 352
Structural Redesign of Artificial Neural Network for Predicting Breast Cancer with the Aid of Artificial Bee Colony
Objectives: In apparent, the core intention is to predict breast cancer stage such as benignant or malignant with different techniques from Breast Cancer Wisconsin (original) benchmark dataset. Methods/Statistical Analysis: When compared through every other tumor, breast cancer is solitary of the actual causes for death in women. To forecast the result of several diseases or find genetic activities of tumors, the breast cancer data could be valuable from the classification. In this work, the proposed method is Artificial Neural Network (ANN) classification with Artificial Bee Colony Optimization (ABC) technique. Findings: Artificial Neural Network (ANN) structure is worked and in this structure training algorithms is utilized and the proposed is Levenberg-Marquardt technique. Artificial Bee Colony Optimization (ABC) technique is used to optimize the hidden layer and neuron of ANN. In the outcome, best validation performance is predicted and the different execution assessment measurements for two optimization algorithms are investigated. Application/Improvements: The comparison performance graph for Accuracy, Sensitivity and Specificity are foreseeing for the most part the precision worth is 95.9% in favor of Artificial Bee Colony Optimization technique.
ANN and ABC, Levenberg-Marquardt.
- Kumar R, Ramachandra and Nagamani. An Efficient Prediction of Breast Cancer Data using Data Mining Techniques. International Journal of Innovations in Engineering and Technology. 2013; 2(4):139-44.
- Helwan A and Abiyev R. ISIBC: An Intelligent System for Identification of Breast Cancer. Proceedings of Advances in Biomedical Engineering (ICABME). 2015; p. 17-20.
- Lu Y, Segelman J, Nordgren A, Lindstrom L, Frisell J and Martling A. Increased risk of colorectal cancer in patients diagnosed with breast cancer in women. The International Journal of Cancer Epidemiology, Detection and Prevention. 2016; 41:57-62.
- You H and Rumbe G. Comparative Study of Classification Techniques on Breast Cancer FNA Biopsy Data. International Journal of Interactive Multimedia and Artificial Intelligence. 2010; 1(3):6-13. Crossref.
- Masood Ahmad A, Muhammad G and Miller J. Breast Cancer Detection Using Cartesian Genetic Programming evolved Artificial Neural Networks. Proceedings of the 14th annual conference on Genetic and evolutionary computation. 2012; p. 1031-8.
- Garg B, Beg S and Ansari. Optimizing Number of Inputs to Classify Breast Cancer Using Artificial Neural Network. Journal of Computer Science and Systems Biology. 2009; 2(4):247-54. Crossref.
- Gomez, Pereira and Infantosi. Evolutionary pulse-coupled neural network for segmenting breast lesions on ultrasonography. Neurocomputing, 2015; 175:877-87.
- Arya C and Tiwari R. Expert System for Breast Cancer Diagnosis: A Survey. Proceedings of International Conference on Computer Communication and Informatics (ICCCI). 2016; p. 1-9. PMCid:PMC4819463.
- Jog N and Mahadik. Implementation of Segmentation and Classification Techniques for Mammogram Images. International Journal of Innovative Research in Science, Engineering and Technology. 2015; 4:422-6.
- Chaurasia S, Chakrabarti P and Chourasia N. An Application of Classification Techniques on Breast Cancer Prognosis. International Journal of Computer Applications. 2012; 59(3):6-10. Crossref.
- Singh Khehra B and Partap Singh Pharwaha A. Classification of Clustered Microcalcifications using MLFFBP-ANN and SVM. Egyptian Informatics Journal. 2016; 17(1):11-20. Crossref.
- Sathya J and Geetha. Mass classification in breast DCE-MR images using an artificial neural network trained via a bee colony optimization algorithm. ScienceAsia. 2013; 39:294306. Crossref.
- Gupta S, Kumar D and Sharma A. Data Mining Classification techniques Applied for Breast Cancer Diagnosis and Prognosis. Indian Journal of Computer Science and Engineering. 2011; 2(2):188-95.
- Chaurasia V and Pal S. A Novel Approach for Breast Cancer Detection using Data Mining Techniques. International Journal of Innovative Research in Computer and Communication Engineering. 2014; 2:2456-65.
- Kharya, Dubey and Soni. Predictive Machine Learning Techniques for Breast Cancer Detection. International Journal of Computer Science and Information Technologies. 2013; 4(6):1023-8.
- Deepa and Devi A. A survey on artificial intelligence approaches for medical image classification. Indian Journal of Science and Technology. 2011; 4(11):1583-95.
- Singh S, Sushmitha, Harini and Surabhi. An Efficient Neural Network Based System for Diagnosis of Breast Cancer. International Journal of Computer Science and Information Technologies. 2014; 5(3):4354-60.
- Narang S, Verma H and Sachdev U. A Review of Breast Cancer Detection using ART Model of Neural Networks. International Journal of Advanced Research in Computer Science and Software Engineering. 2012; 2:311-8.
- Edriss Ebrahim Ali E and Zhi Feng W. Breast Cancer Classification using Support Vector Machine and Neural Network. International Journal of Science and Research. 2016; 5:1-6.
- Mahmood Mina L and Ashidi Mat Isa N. Breast Abnormality Detection in Mammograms Using Artificial Neural Network. Proceedings of International Conference on Computer, Communications, and Control Technology (I4CT). 2015; p. 258-63.
- Wang P, Hu X, Li Y, Liu Q and Zhu X. Automatic cell nuclei segmentation and classification of breast cancer histopathology images. Signal Processing. 2015; 122:1-13. Crossref.
- Ahmad F, Ashidi Mat Isa N, Halim Mohd Noor M and Hussain Z. Intelligent Breast Cancer Diagnosis Using Hybrid GA-ANN. Proceedings of International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN). 2013; p. 9-12. Crossref.
- Singh S, Saini S and Singh M. Cancer Detection using Adaptive Neural Network. International Journal of Advancements in Research and Technology. 2012; 1:1-5.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.