Abstract
Health is so important for human beings. Thanks to the technological developments both in medicine and information technologies, the success percentages of both medical diagnosing and medical treatment systems are increasing day by day. Cancer is the most common causes of death in today’s world and is generally diagnosed at the last stages. Cancer has many types such as breast cancer, skin cancer, leukemia and etc. Diagnosis of cancer at early stages is very important for the success of medical treatments. The aim of this study was to evaluate the classification performances of some popular algorithms on the way to design an efficient computer aided breast and/or skin cancer diagnosing system to support the doctors and patients. For this purpose, same machine learning and deep learning algorithms were applied on immunotherapy dataset and breast cancer Coimbra dataset from UCI machine learning data repository. Feature selection by information gain and reliefF were applied on datasets before classification in order to increase the efficiency of classification processes. Support Vector Machines (SVM), Random Forest (RF), Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) algorithms were used for classification experiments. Accuracy values are used for performance metric. According to these results, RNN has shown the best performance among the others with 92% on both datasets. This shows that deep learning algorithms especially RNN has great potential to diagnose the cancer from dataset with high success ratios.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
R.L. Siegel, K.D. Miller, A. Jemal, Cancer statistics, 2020. CA Cancer J. Clin. 70(1), 7–30 (2020)
F. Bray, J. Ferlay, I. Soerjomataram et al., Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68(6), 394–424 (2018)
F. Khozeimeh, R. Alizadehsani, M. Roshanzamir, A. Khosravi, P. Layegh, S. Nahavandi, An expert system for selecting wart treatment method. Comput. Biol. Med. 81, 167–175 (2017)
Breast Cancer Coimbra Dataset, http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Coimbra
L. Venkataramana, S.G. Jacob, S. Shanmuganathan, V.V.P. Dattuluri, Benchmarking gene selection techniques for prediction of distinct carcinoma from gene expression data: a computational study, in Nature Inspired Computing for Data Science (Springer, Cham, 2020), pp. 241–277
M. Robnik-Šikonja, I. Kononenko, Theoretical and empirical analysis of ReliefF and RReliefF. Mach. Learn. 53(1–2), 23–69 (2003)
I. Kononenko, On biases in estimating multi-valued features, in Ijcai, vol. 95 (1995), pp. 1034–1040
Ç. Elmas, Yapay Sinir Ağları (Kuram, Mimari, Eğitim, Uygulama) (Seçkin Yayıncılık, Ankara, 2003)
O. Song, W. Hu, W. Xie, Robust Support Vector Machine with Bullet Hole Image Classification (IEEE, 2002)
L. Breiman, Some properties of splitting criteria. Mach. Learn. 24(1), 41–47 (1996)
S. Pattanayak, Pro Deep Learning with TensorFlow (Apress, New York, 2017), pp. 153–278. ISBN 978-1-4842-3095-4
A. Sherstinsky, Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Phys. D Nonlinear Phenom. 132306 (2020)
Ş. Kayıkçı, A convolutional neural network model implementation for speech recognition. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 7(3), 1892–1898 (2019)
H. Gunduz, Deep learning-based Parkinson’s disease classification using vocal feature sets. IEEE Access 7, 115540–115551 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Sinan Basarslan, M., Kayaalp, F. (2021). Performance Evaluation of Classification Algorithms on Diagnosis of Breast Cancer and Skin Disease. In: Kose, U., Alzubi, J. (eds) Deep Learning for Cancer Diagnosis. Studies in Computational Intelligence, vol 908. Springer, Singapore. https://doi.org/10.1007/978-981-15-6321-8_2
Download citation
DOI: https://doi.org/10.1007/978-981-15-6321-8_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-6320-1
Online ISBN: 978-981-15-6321-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)