Multi-view convolutional neural network with leader and long-tail particle swarm optimizer for enhancing heart disease a
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S.I. : 2018 INDIA INTL. CONGRESS ON COMPUTATIONAL INTELLIGENCE
Multi-view convolutional neural network with leader and long-tail particle swarm optimizer for enhancing heart disease and breast cancer detection Kun Lan3 • Liansheng Liu2 • Tengyue Li3 • Yuhao Chen3 • Simon Fong3 • Joao Alexandre Lobo Marques4 Raymond K. Wong5 • Rui Tang1
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Received: 26 June 2019 / Accepted: 3 February 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract As the core of deep learning methodologies, convolutional neural network (CNN) has received wide attention in the area of image recognition. In particular, it requires very precise, accurate and fine recognition power for medical imaging processing. Numerous promising prospects of CNN applications with medical prognosis and diagnosis have been reported in the related works, and the common goal among the literature is mainly to analyze the insights from the finest details of medical images and build a more suitable model with maximum accuracy and minimum error. Thus, a novel CNN model is proposed with the characteristics of multi-view feature preprocessing and swarm-based parameter optimization. Additional information of extra features from multi-view is discovered potentially for training, and simultaneously, the most optimal set of CNN parameters are provided by our proposed leader and long-tail-based particle swarm optimization. The purpose of such a hybrid method is to achieve the highest possibility of target recognition in medical images. Preliminary experiments over cardiovascular and mammogram datasets related to heart disease prediction and breast cancer classification, respectively, are designed and conducted, and the results indicate encouraging performance compared to other existing CNN model optimization methods. Keywords Convolutional neural network Leader and long-tail Particle swarm optimization Parameter optimization Heart disease Breast cancer
1 Introduction Kun Lan and Liansheng Liu have contributed equally to this work. & Rui Tang [email protected] 1
Department of Management Science and Information System, Faculty of Management and Economics, Kunming University of Science and Technology, Kunming, Yunnan, China
2
Department of Medical Imaging, First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
3
Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau SAR, China
4
School of Business, University of Saint Joseph, Macau SAR, China
5
School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia
According to the World Health Organization (WHO) in 2018 [1], catastrophic illnesses including breast cancers and heart diseases are the most leading diseases among all global deaths. They make up the most top class of popular diseases in urban and rural livings by affecting individuals of both genders, and the trend of morbidity is becoming higher among
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