TY - GEN
T1 - Comparison between fuzzy kernel c-means, fuzzy kernel possibilistic c-means and support vector machines in soft tissue tumor classification
AU - Rustam, Zuherman
AU - Hartini, Sri
AU - Siswantining, Titin
AU - Utami, Dea Aulia
AU - Putri, Nadisa Karina
N1 - Funding Information:
Acknowledgments. This research was financially supported by the Indonesian Ministry of Research and Higher Education, with a PDUPT 2019 research grant scheme (ID number NKB-1620/UN2.R3.1/HKP.05.00/2019). The authors are grateful to Dr. Sagiran and the staffs from Nur Hidayah Hospital in Yogyakarta, Indonesia for their help in providing the Soft Tissue Tumor (STT) datasets. All reviewers involved in the upgrading of this article are also appreciated.
Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Soft Tissue Tumor (STT) are cell growths, whose existence are not limited to the presence of tumors in soft tissues. Furthermore, they are classified into soft tissue and non-soft tissue tumor and early detection is important to determine the right course of treatment. This research, therefore, aims to compare fuzzy kernel c-means, fuzzy kernel possibilistic c-means and support vector machines on Soft Tissue Tumor dataset, obtained from Nur Hidayah Hospital, Yogyakarta, Indonesia, consisting of 50 STT and 25 non-STT samples. The results conclude that fuzzy kernel c-means provides a better running time when using the parameter σ = 0.05. However, support vector machines, with the parameter σ = 0.0001 performs better than other methods in terms of accuracy, sensitivity, precision, and F1-Score.
AB - Soft Tissue Tumor (STT) are cell growths, whose existence are not limited to the presence of tumors in soft tissues. Furthermore, they are classified into soft tissue and non-soft tissue tumor and early detection is important to determine the right course of treatment. This research, therefore, aims to compare fuzzy kernel c-means, fuzzy kernel possibilistic c-means and support vector machines on Soft Tissue Tumor dataset, obtained from Nur Hidayah Hospital, Yogyakarta, Indonesia, consisting of 50 STT and 25 non-STT samples. The results conclude that fuzzy kernel c-means provides a better running time when using the parameter σ = 0.05. However, support vector machines, with the parameter σ = 0.0001 performs better than other methods in terms of accuracy, sensitivity, precision, and F1-Score.
KW - Classification
KW - Fuzzy kernel C-Means
KW - Fuzzy possibilistic C-Means
KW - Kernel function
KW - Soft tissue tumor
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=85080912761&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-36664-3_11
DO - 10.1007/978-3-030-36664-3_11
M3 - Conference contribution
AN - SCOPUS:85080912761
SN - 9783030366636
T3 - Advances in Intelligent Systems and Computing
SP - 92
EP - 105
BT - Advanced Intelligent Systems for Sustainable Development, AI2SD 2019 - Volume 2 - Advanced Intelligent Systems for Sustainable Development Applied to Agriculture and Health
A2 - Ezziyyani, Mostafa
PB - Springer
T2 - 2nd International Conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2019
Y2 - 8 July 2019 through 11 July 2019
ER -