Ovarian cancer classification accuracy analysis using 15-neuron artificial neural networks model

Md Akizur Rahman; Ravie Chandren Muniyandi; Kh Tohidul Islam; Md Mokhlesur Rahman

doi:10.1109/SCORED.2019.8896332

Ovarian cancer classification accuracy analysis using 15-neuron artificial neural networks model

Md Akizur Rahman, Ravie Chandren Muniyandi, Kh Tohidul Islam, Md Mokhlesur Rahman

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

23 Citations (Scopus)

Abstract

Ovarian cancer is a severe disease for older woman. Based on the research, ovarian cancer is the fifth commonly disease and the seventh causes of death for woman worldwide. For ovarian cancer classification problem, many researchers have performed using Artificial Neural Network (ANN). Classification accuracy is a significant factor for taking decision by the Doctors. Higher classification accuracy can help to take the decision by doctors for giving proper treatment. Accurate and early diagnosis can save lives and reduce the percentage of mortality. This study focuses classification accuracy analysis of ovarian cancer. The purpose of this study is to analyze the classification accuracy using 15-neuron ANN model. The proposed model is benchmarked on ovarian cancer dataset. The achieving result from the proposed model has been compared with the other four classification algorithms. The proposed model has achieved 98.7% ovarian cancer classification accuracy which is more promising and higher than other classification algorithms.

Original language	English
Title of host publication	2019 IEEE Student Conference on Research and Development (SCOReD 2019)
Editors	Moslem Uddin, Maged S. Al-Quraishi, Nivesh Gadipudi, Syed Saad Azhar Ali
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	33-38
Number of pages	6
ISBN (Electronic)	9781728126135
ISBN (Print)	9781728126142
DOIs	https://doi.org/10.1109/SCORED.2019.8896332
Publication status	Published - 2019
Externally published	Yes
Event	IEEE Student Conference on Research and Development (SCOReD) 2019 - Seri Iskandar, Perak, Malaysia Duration: 15 Oct 2019 → 17 Oct 2019 Conference number: 17th https://ieeexplore.ieee.org/xpl/conhome/8890748/proceeding (Proceedings) https://ieeemy.org/scored/ (Website)

Conference

Conference	IEEE Student Conference on Research and Development (SCOReD) 2019
Abbreviated title	SCOReD 2019
Country/Territory	Malaysia
City	Seri Iskandar, Perak
Period	15/10/19 → 17/10/19
Internet address	https://ieeexplore.ieee.org/xpl/conhome/8890748/proceeding (Proceedings) https://ieeemy.org/scored/ (Website)

Keywords

Artificial Neural Network (ANN)
Neuron in Hidden Layer
Ovarian Cancer Classification
Taguchi Method

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/SCORED.2019.8896332

Cite this

Rahman, M. A., Muniyandi, R. C., Islam, K. T., & Rahman, M. M. (2019). Ovarian cancer classification accuracy analysis using 15-neuron artificial neural networks model. In M. Uddin, M. S. Al-Quraishi, N. Gadipudi, & S. S. A. Ali (Eds.), 2019 IEEE Student Conference on Research and Development (SCOReD 2019) (pp. 33-38). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/SCORED.2019.8896332

Rahman, Md Akizur ; Muniyandi, Ravie Chandren ; Islam, Kh Tohidul et al. / Ovarian cancer classification accuracy analysis using 15-neuron artificial neural networks model. 2019 IEEE Student Conference on Research and Development (SCOReD 2019). editor / Moslem Uddin ; Maged S. Al-Quraishi ; Nivesh Gadipudi ; Syed Saad Azhar Ali. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2019. pp. 33-38

@inproceedings{d2d67b592ae44beebe8b3748d4d7c62b,

title = "Ovarian cancer classification accuracy analysis using 15-neuron artificial neural networks model",

abstract = "Ovarian cancer is a severe disease for older woman. Based on the research, ovarian cancer is the fifth commonly disease and the seventh causes of death for woman worldwide. For ovarian cancer classification problem, many researchers have performed using Artificial Neural Network (ANN). Classification accuracy is a significant factor for taking decision by the Doctors. Higher classification accuracy can help to take the decision by doctors for giving proper treatment. Accurate and early diagnosis can save lives and reduce the percentage of mortality. This study focuses classification accuracy analysis of ovarian cancer. The purpose of this study is to analyze the classification accuracy using 15-neuron ANN model. The proposed model is benchmarked on ovarian cancer dataset. The achieving result from the proposed model has been compared with the other four classification algorithms. The proposed model has achieved 98.7% ovarian cancer classification accuracy which is more promising and higher than other classification algorithms.",

keywords = "Artificial Neural Network (ANN), Neuron in Hidden Layer, Ovarian Cancer Classification, Taguchi Method",

author = "Rahman, {Md Akizur} and Muniyandi, {Ravie Chandren} and Islam, {Kh Tohidul} and Rahman, {Md Mokhlesur}",

note = "Funding Information: This work is supported by the Fundamental Research Grant Scheme of the Ministry of Higher Education, Malaysia; Grant code: FGRS/1/2015/ICT04/UKM/02/3. Publisher Copyright: {\textcopyright} 2019 IEEE.; IEEE Student Conference on Research and Development (SCOReD) 2019, SCOReD 2019 ; Conference date: 15-10-2019 Through 17-10-2019",

year = "2019",

doi = "10.1109/SCORED.2019.8896332",

language = "English",

isbn = "9781728126142",

pages = "33--38",

editor = "Moslem Uddin and Al-Quraishi, {Maged S.} and Nivesh Gadipudi and Ali, {Syed Saad Azhar}",

booktitle = "2019 IEEE Student Conference on Research and Development (SCOReD 2019)",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

url = "https://ieeexplore.ieee.org/xpl/conhome/8890748/proceeding, https://ieeemy.org/scored/",

}

Rahman, MA, Muniyandi, RC, Islam, KT & Rahman, MM 2019, Ovarian cancer classification accuracy analysis using 15-neuron artificial neural networks model. in M Uddin, MS Al-Quraishi, N Gadipudi & SSA Ali (eds), 2019 IEEE Student Conference on Research and Development (SCOReD 2019). IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 33-38, IEEE Student Conference on Research and Development (SCOReD) 2019, Seri Iskandar, Perak, Malaysia, 15/10/19. https://doi.org/10.1109/SCORED.2019.8896332

Ovarian cancer classification accuracy analysis using 15-neuron artificial neural networks model. / Rahman, Md Akizur; Muniyandi, Ravie Chandren; Islam, Kh Tohidul et al.
2019 IEEE Student Conference on Research and Development (SCOReD 2019). ed. / Moslem Uddin; Maged S. Al-Quraishi; Nivesh Gadipudi; Syed Saad Azhar Ali. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 33-38.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Ovarian cancer classification accuracy analysis using 15-neuron artificial neural networks model

AU - Rahman, Md Akizur

AU - Muniyandi, Ravie Chandren

AU - Islam, Kh Tohidul

AU - Rahman, Md Mokhlesur

N1 - Conference code: 17th

PY - 2019

Y1 - 2019

N2 - Ovarian cancer is a severe disease for older woman. Based on the research, ovarian cancer is the fifth commonly disease and the seventh causes of death for woman worldwide. For ovarian cancer classification problem, many researchers have performed using Artificial Neural Network (ANN). Classification accuracy is a significant factor for taking decision by the Doctors. Higher classification accuracy can help to take the decision by doctors for giving proper treatment. Accurate and early diagnosis can save lives and reduce the percentage of mortality. This study focuses classification accuracy analysis of ovarian cancer. The purpose of this study is to analyze the classification accuracy using 15-neuron ANN model. The proposed model is benchmarked on ovarian cancer dataset. The achieving result from the proposed model has been compared with the other four classification algorithms. The proposed model has achieved 98.7% ovarian cancer classification accuracy which is more promising and higher than other classification algorithms.

AB - Ovarian cancer is a severe disease for older woman. Based on the research, ovarian cancer is the fifth commonly disease and the seventh causes of death for woman worldwide. For ovarian cancer classification problem, many researchers have performed using Artificial Neural Network (ANN). Classification accuracy is a significant factor for taking decision by the Doctors. Higher classification accuracy can help to take the decision by doctors for giving proper treatment. Accurate and early diagnosis can save lives and reduce the percentage of mortality. This study focuses classification accuracy analysis of ovarian cancer. The purpose of this study is to analyze the classification accuracy using 15-neuron ANN model. The proposed model is benchmarked on ovarian cancer dataset. The achieving result from the proposed model has been compared with the other four classification algorithms. The proposed model has achieved 98.7% ovarian cancer classification accuracy which is more promising and higher than other classification algorithms.

KW - Artificial Neural Network (ANN)

KW - Neuron in Hidden Layer

KW - Ovarian Cancer Classification

KW - Taguchi Method

UR - http://www.scopus.com/inward/record.url?scp=85075635300&partnerID=8YFLogxK

U2 - 10.1109/SCORED.2019.8896332

DO - 10.1109/SCORED.2019.8896332

M3 - Conference Paper

AN - SCOPUS:85075635300

SN - 9781728126142

SP - 33

EP - 38

BT - 2019 IEEE Student Conference on Research and Development (SCOReD 2019)

A2 - Uddin, Moslem

A2 - Al-Quraishi, Maged S.

A2 - Gadipudi, Nivesh

A2 - Ali, Syed Saad Azhar

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

T2 - IEEE Student Conference on Research and Development (SCOReD) 2019

Y2 - 15 October 2019 through 17 October 2019

ER -

Rahman MA, Muniyandi RC, Islam KT, Rahman MM. Ovarian cancer classification accuracy analysis using 15-neuron artificial neural networks model. In Uddin M, Al-Quraishi MS, Gadipudi N, Ali SSA, editors, 2019 IEEE Student Conference on Research and Development (SCOReD 2019). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2019. p. 33-38 doi: 10.1109/SCORED.2019.8896332

Ovarian cancer classification accuracy analysis using 15-neuron artificial neural networks model

Abstract

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Cite this