{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T07:31:08Z","timestamp":1768548668860,"version":"3.49.0"},"reference-count":19,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2016,11,1]],"date-time":"2016-11-01T00:00:00Z","timestamp":1477958400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Intelligent Decision Technologies"],"published-print":{"date-parts":[[2016,11]]},"abstract":"<jats:p>This work intends to an integration of implementing an automated diagnostic systems for breast cancer detection using Artificial Neural Network (ANN) in FPGA. In the world, breast cancer is the fifth most common cause of cancer death. So better classification system is needed for diagnosing breast cancer disease. In this work, the training and testing of the Multilayer Perceptron Neural Network (MLPNN) with Back Propagation Network (BPN) is done with the attributes of the record of the Wisconsin Breast Cancer Database (WBCD). The neural network lacks the flexibility during off line training. In order to overcome the flexibility, it is necessary to train and test the network on on-chip neural network using FPGA. The purpose is to determine the cancer of patients either having benign or malignant through an FPGA based implementation of smart instrument. In order to implement the hardware, VERILOG coding is done for ANN and synthesized by Xilinx family XC5VLX50TFFT1136 FPGA Virtex 5 board using XILINX ISE tool to get the netlist of ANN. Finally the netlist is mapped to FPGA and the hardware functionality is verified. The correct classification rate of proposed system is 90.83%.<\/jats:p>","DOI":"10.3233\/idt-160261","type":"journal-article","created":{"date-parts":[[2016,7,5]],"date-time":"2016-07-05T10:04:48Z","timestamp":1467713088000},"page":"341-352","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["FPGA implementation of on-chip ANN for breast cancer diagnosis"],"prefix":"10.1177","volume":"10","author":[{"given":"D.","family":"Selvathi","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India"}]},{"given":"R. Deiva","family":"Nayagam","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India"}]}],"member":"179","published-online":{"date-parts":[[2016,11]]},"reference":[{"key":"e_1_2_1_2_2","doi-asserted-by":"crossref","unstructured":"YoussefA. MohammedK. and NassarA. A Reconfigurable Generic and Programmable Feed Forward Neural-network IEEE 14th International Conference on Modelling and Simulation 2012.","DOI":"10.1109\/UKSim.2012.12"},{"key":"e_1_2_1_3_2","article-title":"Design and Realization of FPGA based Off-Chip Trained MLP for Classical XOR Problem and Need of On-Chip Training","author":"Packia L.K.","year":"2012","unstructured":"PackiaL.K. and SubadraM., Design and Realization of FPGA based Off-Chip Trained MLP for Classical XOR Problem and Need of On-Chip Training, Special Issue of International Journal of Computer Applications on International Conference on Electronics, Communication and Information Systems (ICECI 12), 2012.","journal-title":"Special Issue of International Journal of Computer Applications on International Conference on Electronics, Communication and Information Systems (ICECI 12)"},{"key":"e_1_2_1_4_2","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/274\/1\/012051"},{"key":"e_1_2_1_5_2","volume-title":"Machine learning, neural and statistical classification","author":"Michie D.","year":"1994","unstructured":"MichieD., SpiegelhalterD.J. and TayorC.C., Machine learning, neural and statistical classification, London: Ellis Horwood, 1994."},{"key":"e_1_2_1_6_2","doi-asserted-by":"publisher","DOI":"10.1080\/10556789208805504"},{"key":"e_1_2_1_7_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-5915.1981.tb00061.x"},{"key":"e_1_2_1_8_2","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.19.3.272"},{"key":"e_1_2_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TC.1968.229395"},{"key":"e_1_2_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2006.06.031"},{"key":"e_1_2_1_11_2","first-page":"4","article-title":"An introduction of computing with neural nets","author":"Lippmann R.P.","year":"1992","unstructured":"LippmannR.P., An introduction of computing with neural nets, IEEE ASSP Magazine, 1992, pp. 4-22.","journal-title":"IEEE ASSP Magazine"},{"key":"e_1_2_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/78.157194"},{"key":"e_1_2_1_13_2","doi-asserted-by":"crossref","unstructured":"WidrowB. and LehrM.A. 30 years of adaptive neural networks: perceptron madaline and back propagation Proceedings of the IEEE Neural Networks Conference78 (1990) 1415-1442.","DOI":"10.1109\/5.58323"},{"key":"e_1_2_1_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2007.07.004"},{"key":"e_1_2_1_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2007.11.004"},{"key":"e_1_2_1_16_2","volume-title":"Introduction to Fuzzy Arithmetic, Theory and Applications","author":"Kaufmann A.","year":"1985","unstructured":"KaufmannA. and GuptaM.M., Introduction to Fuzzy Arithmetic, Theory and Applications, Van Nostrand Reinhold Co., 1985."},{"key":"e_1_2_1_17_2","volume-title":"Fuzzy Theory Systems: Techniques and Applications","author":"Konar A.","year":"1999","unstructured":"KonarA. and PalS., Modeling cognition with fuzzy neural nets: LeondesC.T. (Ed.), Fuzzy Theory Systems: Techniques and Applications, 3, Academic Press, 1999."},{"key":"e_1_2_1_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2006.08.005"},{"key":"e_1_2_1_19_2","first-page":"45","article-title":"FPGA Implementation of a Trained Neural Network","volume":"10","author":"Simgh S.","year":"2015","unstructured":"SimghS., SanjeeviS., SumaV. and AkhilT.V., FPGA Implementation of a Trained Neural Network, IOSR Journal of Electronics and Communication Engineering10 (2015), 45-54.","journal-title":"IOSR Journal of Electronics and Communication Engineering"},{"key":"e_1_2_1_20_2","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/818243"}],"container-title":["Intelligent Decision Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IDT-160261","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IDT-160261","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T10:32:45Z","timestamp":1754562765000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/IDT-160261"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,11]]},"references-count":19,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2016,11]]}},"alternative-id":["10.3233\/IDT-160261"],"URL":"https:\/\/doi.org\/10.3233\/idt-160261","relation":{},"ISSN":["1872-4981","1875-8843"],"issn-type":[{"value":"1872-4981","type":"print"},{"value":"1875-8843","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,11]]}}}